Methods for non-invasive prenatal ploidy calling

ABSTRACT

The present disclosure provides methods for determining the ploidy status of a chromosome in a gestating fetus from genotypic data measured from a mixed sample of DNA comprising DNA from both the mother of the fetus and from the fetus, and optionally from genotypic data from the mother and father. The ploidy state is determined by using a joint distribution model to create a plurality of expected allele distributions for different possible fetal ploidy states given the parental genotypic data, and comparing the expected allelic distributions to the pattern of measured allelic distributions measured in the mixed sample, and choosing the ploidy state whose expected allelic distribution pattern most closely matches the observed allelic distribution pattern. The mixed sample of DNA may be preferentially enriched at a plurality of polymorphic loci in a way that minimizes the allelic bias, for example using massively multiplexed targeted PCR.

RELATED APPLICATIONS

This application is a continuation of U.S. Utility application Ser. No.16/289,528, filed Feb. 28, 2019. U.S. Utility application Ser. No.16/289,528 is a continuation of U.S. Utility application Ser. No.15/586,013, filed May 3, 2017. U.S. Utility application Ser. No.15/586,013 is a continuation of U.S. Utility application Ser. No.14/532,666, filed Nov. 4, 2014. U.S. Utility application Ser. No.14/532,666 is a continuation of U.S. Utility application Ser. No.13/791,397, filed Mar. 8, 2013, now U.S. Pat. No. 9,163,282. U.S.Utility application Ser. No. 13/791,397, now U.S. Pat. No. 9,163,282, isa continuation of U.S. Utility application Ser. No. 13/300,235, filedNov. 18, 2011, now U.S. Pat. No. 10,017,812. U.S. Utility applicationSer. No. 13/300,235, now U.S. Pat. No. 10,017,812, is acontinuation-in-part of U.S. Utility application Ser. No. 13/110,685,filed May 18, 2011, now U.S. Pat. No. 8,825,412, and also claims thebenefit of U.S. Provisional Application Ser. No. 61/571,248, filed Jun.23, 2011; and U.S. Provisional Application Ser. No. 61/542,508, filedOct. 3, 2011. U.S. Utility application Ser. No. 13/110,685, now U.S.Pat. No. 8,825,412, claims the benefit of U.S. Provisional ApplicationSer. No. 61/395,850, filed May 18, 2010; U.S. Provisional ApplicationSer. No. 61/398,159, filed Jun. 21, 2010; U.S. Provisional ApplicationSer. No. 61/462,972, filed Feb. 9, 2011; U.S. Provisional ApplicationSer. No. 61/448,547, filed Mar. 2, 2011; and U.S. ProvisionalApplication Ser. No. 61/516,996, filed Apr. 12, 2011. The entirety ofall these applications are hereby incorporated herein by reference forthe teachings therein.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted electronically in ASCII format and is hereby incorporated byreference in its entirety. Said ASCII copy, created on Apr. 30, 2019, isnamed N_004_US_44_SL.txt and is 2,970 bytes in size.

FIELD

The present disclosure relates generally to methods for non-invasiveprenatal ploidy calling.

BACKGROUND

Current methods of prenatal diagnosis can alert physicians and parentsto abnormalities in growing fetuses. Without prenatal diagnosis, one in50 babies is born with serious physical or mental handicap, and as manyas one in 30 will have some form of congenital malformation.Unfortunately, standard methods have either poor accuracy, or involve aninvasive procedure that carries a risk of miscarriage. Methods based onmaternal blood hormone levels or ultrasound measurements arenon-invasive, however, they also have low accuracies. Methods such asamniocentesis, chorion villus biopsy and fetal blood sampling have highaccuracy, but are invasive and carry significant risks. Amniocentesiswas performed in approximately 3% of all pregnancies in the US, thoughits frequency of use has been decreasing over the past decade and ahalf.

It has recently been discovered that cell-free fetal DNA and intactfetal cells can enter maternal blood circulation. Consequently, analysisof this genetic material can allow early Non-Invasive Prenatal GeneticDiagnosis (NPD).

Normal humans have two sets of 23 chromosomes in every healthy, diploidcell, with one copy coming from each parent. Aneuploidy, a condition ina nuclear cell where the cell contains too many and/or too fewchromosomes is believed to be responsible for a large percentage offailed implantations, miscarriages, and genetic diseases. Detection ofchromosomal abnormalities can identify individuals or embryos withconditions such as Down syndrome, Klinefelter's syndrome, and Turnersyndrome, among others, in addition to increasing the chances of asuccessful pregnancy. Testing for chromosomal abnormalities isespecially important as the mother's age: between the ages of 35 and 40it is estimated that at least 40% of the embryos are abnormal, and abovethe age of 40, more than half of the embryos are abnormal.

Some Tests Used for Prenatal Screening

Low levels of pregnancy-associated plasma protein A (PAPP-A) as measuredin maternal serum during the first trimester may be associated withfetal chromosomal anomalies including trisomies 13, 18, and 21. Inaddition, low PAPP-A levels in the first trimester may predict anadverse pregnancy outcome, including a small for gestational age (SGA)baby or stillbirth. Pregnant women often undergo the first trimesterserum screen, which commonly involves testing women for blood levels ofthe hormones PAPP-A and beta human chorionic gonadotropin (beta-hCG). Insome cases women are also given an ultrasound to look for possiblephysiological defects. In particular, the nuchal translucency (NT)measurement can indicate risk of aneuploidy in a fetus. In many areas,the standard of treatment for prenatal screening includes the firsttrimester serum screen combined with an NT test.

The triple test, also called triple screen, the Kettering test or theBart's test, is an investigation performed during pregnancy in thesecond trimester to classify a patient as either high-risk or low-riskfor chromosomal abnormalities (and neural tube defects). The term“multiple-marker screening test” is sometimes used instead. The term“triple test” can encompass the terms “double test,” “quadruple test,”“quad test” and “penta test.”

The triple test measures serum levels of alpha-fetoprotein (AFP),unconjugated estriol (UE3), beta human chorionic gonadotropin(beta-hCG), Invasive Trophoblast Antigen (ITA) and/or inhibin. Apositive test means having a high risk of chromosomal abnormalities (andneural tube defects), and such patients are then referred for moresensitive and specific procedures to receive a definitive diagnosis,mostly invasive procedures like amniocentesis. The triple test can beused to screen for a number of conditions, including trisomy 21 (Downsyndrome). In addition to Down syndrome, the triple and quadruple testsscreen for fetal trisomy 18 also known as Edward's syndrome, open neuraltube defects, and may also detect an increased risk of Turner syndrome,triploidy, trisomy 16 mosaicism, fetal death, Smith-Lemli-Opitzsyndrome, and steroid sulfatase deficiency.

SUMMARY

Disclosed herein are methods for determining a ploidy status of achromosome in a gestating fetus. According to aspects illustratedherein, in an embodiment a method for determining a ploidy status of achromosome in a gestating fetus includes obtaining a first sample of DNAthat comprises maternal DNA from the mother of the fetus and fetal DNAfrom the fetus, preparing the first sample by isolating the DNA so as toobtain a prepared sample, measuring the DNA in the prepared sample at aplurality of polymorphic loci on the chromosome, calculating, on acomputer, allele counts at the plurality of polymorphic loci from theDNA measurements made on the prepared sample, creating, on a computer, aplurality of ploidy hypotheses each pertaining to a different possibleploidy state of the chromosome, building, on a computer, a jointdistribution model for the expected allele counts at the plurality ofpolymorphic loci on the chromosome for each ploidy hypothesis,determining, on a computer, a relative probability of each of the ploidyhypotheses using the joint distribution model and the allele countsmeasured on the prepared sample, and calling the ploidy state of thefetus by selecting the ploidy state corresponding to the hypothesis withthe greatest probability.

In some embodiments, the DNA in the first sample originates frommaternal plasma. In some embodiments, preparing the first sample furthercomprises amplifying the DNA. In some embodiments, preparing the firstsample further comprises preferentially enriching the DNA in the firstsample at a plurality of polymorphic loci.

In some embodiments, preferentially enriching the DNA in the firstsample at the plurality of polymorphic loci includes obtaining aplurality of pre-circularized probes where each probe targets one of thepolymorphic loci, and where the 3′ and 5′ end of the probes are designedto hybridize to a region of DNA that is separated from the polymorphicsite of the locus by a small number of bases, where the small number is1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21 to 25, 26 to 30, 31 to 60, or a combination thereof, hybridizing thepre-circularized probes to DNA from the first sample, filling the gapbetween the hybridized probe ends using DNA polymerase, circularizingthe pre-circularized probe, and amplifying the circularized probe.

In some embodiments, the preferentially enriching the DNA at theplurality of polymorphic loci includes obtaining a plurality ofligation-mediated PCR probes where each PCR probe targets one of thepolymorphic loci, and where the upstream and downstream PCR probes aredesigned to hybridize to a region of DNA, on one strand of DNA, that isseparated from the polymorphic site of the locus by a small number ofbases, where the small number is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21 to 25, 26 to 30, 31 to 60, or acombination thereof, hybridizing the ligation-mediated PCR probes to theDNA from the first sample, filling the gap between the ligation-mediatedPCR probe ends using DNA polymerase, ligating the ligation-mediated PCRprobes, and amplifying the ligated ligation-mediated PCR probes.

In some embodiments, preferentially enriching the DNA at the pluralityof polymorphic loci includes obtaining a plurality of hybrid captureprobes that target the polymorphic loci, hybridizing the hybrid captureprobes to the DNA in the first sample and physically removing some orall of the unhybridized DNA from the first sample of DNA.

In some embodiments, the hybrid capture probes are designed to hybridizeto a region that is flanking but not overlapping the polymorphic site.In some embodiments, the hybrid capture probes are designed to hybridizeto a region that is flanking but not overlapping the polymorphic site,and where the length of the flanking capture probe may be selected fromthe group consisting of less than about 120 bases, less than about 110bases, less than about 100 bases, less than about 90 bases, less thanabout 80 bases, less than about 70 bases, less than about 60 bases, lessthan about 50 bases, less than about 40 bases, less than about 30 bases,and less than about 25 bases. In some embodiments, the hybrid captureprobes are designed to hybridize to a region that overlaps thepolymorphic site, and where the plurality of hybrid capture probescomprise at least two hybrid capture probes for each polymorphic loci,and where each hybrid capture probe is designed to be complementary to adifferent allele at that polymorphic locus.

In some embodiments, preferentially enriching the DNA at a plurality ofpolymorphic loci includes obtaining a plurality of inner forward primerswhere each primer targets one of the polymorphic loci, and where the 3′end of the inner forward primers are designed to hybridize to a regionof DNA upstream from the polymorphic site, and separated from thepolymorphic site by a small number of bases, where the small number isselected from the group consisting of 1, 2, 3, 4, 5, 6 to 10, 11 to 15,16 to 20, 21 to 25, 26 to 30, or 31 to 60 base pairs, optionallyobtaining a plurality of inner reverse primers where each primer targetsone of the polymorphic loci, and where the 3′ end of the inner reverseprimers are designed to hybridize to a region of DNA upstream from thepolymorphic site, and separated from the polymorphic site by a smallnumber of bases, where the small number is selected from the groupconsisting of 1, 2, 3, 4, 5, 6 to 10, 11 to 15, 16 to 20, 21 to 25, 26to 30, or 31 to 60 base pairs, hybridizing the inner primers to the DNA,and amplifying the DNA using the polymerase chain reaction to formamplicons.

In some embodiments, the method also includes obtaining a plurality ofouter forward primers where each primer targets one of the polymorphicloci, and where the outer forward primers are designed to hybridize tothe region of DNA upstream from the inner forward primer, optionallyobtaining a plurality of outer reverse primers where each primer targetsone of the polymorphic loci, and where the outer reverse primers aredesigned to hybridize to the region of DNA immediately downstream fromthe inner reverse primer, hybridizing the first primers to the DNA, andamplifying the DNA using the polymerase chain reaction.

In some embodiments, the method also includes obtaining a plurality ofouter reverse primers where each primer targets one of the polymorphicloci, and where the outer reverse primers are designed to hybridize tothe region of DNA immediately downstream from the inner reverse primer,optionally obtaining a plurality of outer forward primers where eachprimer targets one of the polymorphic loci, and where the outer forwardprimers are designed to hybridize to the region of DNA upstream from theinner forward primer, hybridizing the first primers to the DNA, andamplifying the DNA using the polymerase chain reaction.

In some embodiments, preparing the first sample further includesappending universal adapters to the DNA in the first sample andamplifying the DNA in the first sample using the polymerase chainreaction. In some embodiments, at least a fraction of the amplicons thatare amplified are less than 100 bp, less than 90 bp, less than 80 bp,less than 70 bp, less than 65 bp, less than 60 bp, less than 55 bp, lessthan 50 bp, or less than 45 bp, and where the fraction is 10%, 20%, 30%,40%, 50%, 60%, 70%, 80%, 90%, or 99%.

In some embodiments, amplifying the DNA is done in one or a plurality ofindividual reaction volumes, and where each individual reaction volumecontains more than 100 different forward and reverse primer pairs, morethan 200 different forward and reverse primer pairs, more than 500different forward and reverse primer pairs, more than 1,000 differentforward and reverse primer pairs, more than 2,000 different forward andreverse primer pairs, more than 5,000 different forward and reverseprimer pairs, more than 10,000 different forward and reverse primerpairs, more than 20,000 different forward and reverse primer pairs, morethan 50,000 different forward and reverse primer pairs, or more than100,000 different forward and reverse primer pairs.

In some embodiments, preparing the first sample further comprisesdividing the first sample into a plurality of portions, and where theDNA in each portion is preferentially enriched at a subset of theplurality of polymorphic loci. In some embodiments, the inner primersare selected by identifying primer pairs likely to form undesired primerduplexes and removing from the plurality of primers at least one of thepair of primers identified as being likely to form undesired primerduplexes. In some embodiments, the inner primers contain a region thatis designed to hybridize either upstream or downstream of the targetedpolymorphic locus, and optionally contain a universal priming sequencedesigned to allow PCR amplification. In some embodiments, at least someof the primers additionally contain a random region that differs foreach individual primer molecule. In some embodiments, at least some ofthe primers additionally contain a molecular barcode.

In some embodiments, the method also includes obtaining genotypic datafrom one or both parents of the fetus. In some embodiments, obtaininggenotypic data from one or both parents of the fetus includes preparingthe DNA from the parents where the preparing comprises preferentiallyenriching the DNA at the plurality of polymorphic loci to give preparedparental DNA, optionally amplifying the prepared parental DNA, andmeasuring the parental DNA in the prepared sample at the plurality ofpolymorphic loci.

In some embodiments, building a joint distribution model for theexpected allele count probabilities of the plurality of polymorphic locion the chromosome is done using the obtained genetic data from the oneor both parents. In some embodiments, the first sample has been isolatedfrom maternal plasma and where the obtaining genotypic data from themother is done by estimating the maternal genotypic data from the DNAmeasurements made on the prepared sample.

In some embodiments, preferential enrichment results in average degreeof allelic bias between the prepared sample and the first sample of afactor selected from the group consisting of no more than a factor of 2,no more than a factor of 1.5, no more than a factor of 1.2, no more thana factor of 1.1, no more than a factor of 1.05, no more than a factor of1.02, no more than a factor of 1.01, no more than a factor of 1.005, nomore than a factor of 1.002, no more than a factor of 1.001 and no morethan a factor of 1.0001. In some embodiments, the plurality ofpolymorphic loci are SNPs. In some embodiments, measuring the DNA in theprepared sample is done by sequencing.

In some embodiments, a diagnostic box is disclosed for helping todetermine a ploidy status of a chromosome in a gestating fetus where thediagnostic box is capable of executing the preparing and measuring stepsof the method of claim 1.

In some embodiments, the allele counts are probabilistic rather thanbinary. In some embodiments, measurements of the DNA in the preparedsample at the plurality of polymorphic loci are also used to determinewhether or not the fetus has inherited one or a plurality of diseaselinked haplotypes.

In some embodiments, building a joint distribution model for allelecount probabilities is done by using data about the probability ofchromosomes crossing over at different locations in a chromosome tomodel dependence between polymorphic alleles on the chromosome. In someembodiments, building a joint distribution model for allele counts andthe step of determining the relative probability of each hypothesis aredone using a method that does not require the use of a referencechromosome.

In some embodiments, determining the relative probability of eachhypothesis makes use of an estimated fraction of fetal DNA in theprepared sample. In some embodiments, the DNA measurements from theprepared sample used in calculating allele count probabilities anddetermining the relative probability of each hypothesis comprise primarygenetic data. In some embodiments, selecting the ploidy statecorresponding to the hypothesis with the greatest probability is carriedout using maximum likelihood estimates or maximum a posterioriestimates.

In some embodiments, calling the ploidy state of the fetus also includescombining the relative probabilities of each of the ploidy hypothesesdetermined using the joint distribution model and the allele countprobabilities with relative probabilities of each of the ploidyhypotheses that are calculated using statistical techniques taken from agroup consisting of a read count analysis, comparing heterozygosityrates, a statistic that is only available when parental geneticinformation is used, the probability of normalized genotype signals forcertain parent contexts, a statistic that is calculated using anestimated fetal fraction of the first sample or the prepared sample, andcombinations thereof.

In some embodiments, a confidence estimate is calculated for the calledploidy state. In some embodiments, the method also includes taking aclinical action based on the called ploidy state of the fetus, whereinthe clinical action is selected from one of terminating the pregnancy ormaintaining the pregnancy.

In some embodiments, the method may be performed for fetuses at between4 and 5 weeks gestation; between 5 and 6 weeks gestation; between 6 and7 weeks gestation; between 7 and 8 weeks gestation; between 8 and 9weeks gestation; between 9 and 10 weeks gestation; between 10 and 12weeks gestation; between 12 and 14 weeks gestation; between 14 and 20weeks gestation; between 20 and 40 weeks gestation; in the firsttrimester; in the second trimester; in the third trimester; orcombinations thereof.

In some embodiments, a report displaying a determined ploidy status of achromosome in a gestating fetus generated using the method. In someembodiments, a kit is disclosed for determining a ploidy status of atarget chromosome in a gestating fetus designed to be used with themethod of claim 9, the kit including a plurality of inner forwardprimers and optionally the plurality of inner reverse primers, whereeach of the primers is designed to hybridize to the region of DNAimmediately upstream and/or downstream from one of the polymorphic siteson the target chromosome, and optionally additional chromosomes, wherethe region of hybridization is separated from the polymorphic site by asmall number of bases, where the small number is selected from the groupconsisting of 1, 2, 3, 4, 5, 6 to 10, 11 to 15, 16 to 20, 21 to 25, 26to 30, 31 to 60, and combinations thereof.

In some embodiments, a method is disclosed for determining presence orabsence of fetal aneuploidy in a maternal tissue sample comprising fetaland maternal genomic DNA, the method including (a) obtaining a mixtureof fetal and maternal genomic DNA from said maternal tissue sample, (b)conducting massively parallel DNA sequencing of DNA fragments randomlyselected from the mixture of fetal and maternal genomic DNA of step a)to determine the sequence of said DNA fragments, (c) identifyingchromosomes to which the sequences obtained in step b) belong, (d) usingthe data of step c) to determine an amount of at least one firstchromosome in said mixture of maternal and fetal genomic DNA, whereinsaid at least one first chromosome is presumed to be euploid in thefetus, (e) using the data of step c) to determine an amount of a secondchromosome in said mixture of maternal and fetal genomic DNA, whereinsaid second chromosome is suspected to be aneuploid in the fetus, (f)calculating the fraction of fetal DNA in the mixture of fetal andmaternal DNA, (g) calculating an expected distribution of the amount ofthe second target chromosome if the second target chromosome is euploid,using the number in step d), (h) calculating an expected distribution ofthe amount of the second target chromosome if the second targetchromosome is aneuploid, using the first number is step d) and thecalculated fraction of fetal DNA in the mixture of fetal and maternalDNA in step f), and (i) using a maximum likelihood or maximum aposteriori approach to determine whether the amount of the secondchromosome as determined in step e) is more likely to be part of thedistribution calculated in step g) or the distribution calculated instep h); thereby indicating the presence or absence of a fetalaneuploidy.

BRIEF DESCRIPTION OF THE DRAWINGS

The presently disclosed embodiments will be further explained withreference to the attached drawings, wherein like structures are referredto by like numerals throughout the several views. The drawings shown arenot necessarily to scale, with emphasis instead generally being placedupon illustrating the principles of the presently disclosed embodiments.

FIG. 1: Graphical representation of direct multiplexed mini-PCR method.

FIG. 2: Graphical representation of semi-nested mini-PCR method.

FIG. 3: Graphical representation of fully nested mini-PCR method.

FIG. 4: Graphical representation of hemi-nested mini-PCR method.

FIG. 5: Graphical representation of triply hemi-nested mini-PCR method.

FIG. 6: Graphical representation of one-sided nested mini-PCR method.

FIG. 7: Graphical representation of one-sided mini-PCR method.

FIG. 8: Graphical representation of reverse semi-nested mini-PCR method.

FIG. 9: Some possible workflows for semi-nested methods.

FIG. 10: Graphical representation of looped ligation adaptors.

FIG. 11: Graphical representation of internally tagged primers.

FIG. 12: An example of some primers with internal tags.

FIG. 13: Graphical representation of a method using primers with aligation adaptor binding region.

FIG. 14: Simulated ploidy call accuracies for counting method with twodifferent analysis techniques.

FIG. 15: Ratio of two alleles for a plurality of SNPs in a cell line inExperiment 4.

FIG. 16: Ratio of two alleles for a plurality of SNPs in a cell line inExperiment 4 sorted by chromosome.

FIGS. 17A-17D: Ratio of two alleles for a plurality of SNPs in fourpregnant women plasma samples, sorted by chromosome.

FIG. 18: Fraction of data that can be explained by binomial variancebefore and after data correction.

FIG. 19: Graph showing relative enrichment of fetal DNA in samplesfollowing a short library preparation protocol.

FIG. 20: Depth of read graph comparing direct PCR and semi-nestedmethods.

FIG. 21: Comparison of depth of read for direct PCR of three genomicsamples.

FIG. 22: Comparison of depth of read for semi-nested mini-PCR of threesamples.

FIG. 23: Comparison of depth of read for 1,200-plex and 9,600-plexreactions.

FIG. 24: Read count ratios for six cells at three chromosomes.

FIGS. 25A-25C: Allele ratios for two three-cell reactions (FIGS. 25B and25C) and a third reaction run on 1 ng of genomic DNA at threechromosomes (FIG. 25A).

FIGS. 26A and 26B: Allele ratios for two single-cell reactions (FIGS.26A and 26B) at three chromosomes.

While the above-identified drawings set forth presently disclosedembodiments, other embodiments are also contemplated, as noted in thediscussion. This disclosure presents illustrative embodiments by way ofrepresentation and not limitation. Numerous other modifications andembodiments can be devised by those skilled in the art which fall withinthe scope and spirit of the principles of the presently disclosedembodiments.

DETAILED DESCRIPTION

In an embodiment, the present disclosure provides ex vivo methods fordetermining the ploidy status of a chromosome in a gestating fetus fromgenotypic data measured from a mixed sample of DNA (i.e., DNA from themother of the fetus, and DNA from the fetus) and optionally fromgenotypic data measured from a sample of genetic material from themother and possibly also from the father, wherein the determining isdone by using a joint distribution model to create a set of expectedallele distributions for different possible fetal ploidy states giventhe parental genotypic data, and comparing the expected allelicdistributions to the actual allelic distributions measured in the mixedsample, and choosing the ploidy state whose expected allelicdistribution pattern most closely matches the observed allelicdistribution pattern. In an embodiment, the mixed sample is derived frommaternal blood, or maternal serum or plasma. In an embodiment, the mixedsample of DNA may be preferentially enriched at a plurality ofpolymorphic loci. In an embodiment, the preferential enrichment is donein a way that minimizes the allelic bias. In an embodiment, the presentdisclosure relates to a composition of DNA that has been preferentiallyenriched at a plurality of loci such that the allelic bias is low. In anembodiment, the allelic distribution(s) are measured by sequencing theDNA from the mixed sample. In an embodiment, the joint distributionmodel assumes that the alleles will be distributed in a binomialfashion. In an embodiment, the set of expected joint alleledistributions are created for genetically linked loci while consideringthe extant recombination frequencies from various sources, for example,using data from the International HapMap Consortium.

In an embodiment, the present disclosure provides methods fornon-invasive prenatal diagnosis (NPD), specifically, determining theaneuploidy status of a fetus by observing allele measurements at aplurality of polymorphic loci in genotypic data measured on DNAmixtures, where certain allele measurements are indicative of ananeuploid fetus, while other allele measurements are indicative of aeuploid fetus. In an embodiment, the genotypic data is measured bysequencing DNA mixtures that were derived from maternal plasma. In anembodiment, the DNA sample may be preferentially enriched in moleculesof DNA that correspond to the plurality of loci whose alleledistributions are being calculated. In an embodiment a sample of DNAcomprising only or almost only genetic material from the mother andpossibly also a sample of DNA comprising only or almost only geneticmaterial from the father are measured. In an embodiment, the geneticmeasurements of one or both parents along with the estimated fetalfraction are used to create a plurality of expected allele distributionscorresponding to different possible underlying genetic states of thefetus; the expected allele distributions may be termed hypotheses. In anembodiment, the maternal genetic data is not determined by measuringgenetic material that is exclusively or almost exclusively maternal innature, rather, it is estimated from the genetic measurements made onmaternal plasma that comprises a mixture of maternal and fetal DNA. Insome embodiments the hypotheses may comprise the ploidy of the fetus atone or more chromosomes, which segments of which chromosomes in thefetus were inherited from which parents, and combinations thereof. Insome embodiments, the ploidy state of the fetus is determined bycomparing the observed allele measurements to the different hypotheseswhere at least some of the hypotheses correspond to different ploidystates, and selecting the ploidy state that corresponds to thehypothesis that is most likely to be true given the observed allelemeasurements. In an embodiment, this method involves using allelemeasurement data from some or all measured SNPs, regardless of whetherthe loci are homozygous or heterozygous, and therefore does not involveusing alleles at loci that are only heterozygous. This method may not beappropriate for situations where the genetic data pertains to only onepolymorphic locus. This method is particularly advantageous when thegenetic data comprises data for more than ten polymorphic loci for atarget chromosome or more than twenty polymorphic loci. This method isespecially advantageous when the genetic data comprises data for morethan 50 polymorphic loci for a target chromosome, more than 100polymorphic loci or more than 200 polymorphic loci for a targetchromosome. In some embodiments, the genetic data may comprise data formore than 500 polymorphic loci for a target chromosome, more than 1,000polymorphic loci, more than 2,000 polymorphic loci, or more than 5,000polymorphic loci for a target chromosome.

In an embodiment, a method disclosed herein uses selective enrichmenttechniques that preserve the relative allele frequencies that arepresent in the original sample of DNA at each polymorphic locus from aset of polymorphic loci. In some embodiments the amplification and/orselective enrichment technique may involve PCR such as ligation mediatedPCR, fragment capture by hybridization, MOLECULAR INVERSION PROBES, orother circularizing probes. In some embodiments, methods foramplification or selective enrichment may involve using probes where,upon correct hybridization to the target sequence, the 3-prime end or5-prime end of a nucleotide probe is separated from the polymorphic siteof the allele by a small number of nucleotides. This separation reducespreferential amplification of one allele, termed allele bias. This is animprovement over methods that involve using probes where the 3-prime endor 5-prime end of a correctly hybridized probe are directly adjacent toor very near to the polymorphic site of an allele. In an embodiment,probes in which the hybridizing region may or certainly contains apolymorphic site are excluded. Polymorphic sites at the site ofhybridization can cause unequal hybridization or inhibit hybridizationaltogether in some alleles, resulting in preferential amplification ofcertain alleles. These embodiments are improvements over other methodsthat involve targeted amplification and/or selective enrichment in thatthey better preserve the original allele frequencies of the sample ateach polymorphic locus, whether the sample is pure genomic sample from asingle individual or mixture of individuals.

In an embodiment, a method disclosed herein uses highly efficient highlymultiplexed targeted PCR to amplify DNA followed by high throughputsequencing to determine the allele frequencies at each target locus. Theability to multiplex more than about 50 or 100 PCR primers in onereaction in a way that most of the resulting sequence reads map totargeted loci is novel and non-obvious. One technique that allows highlymultiplexed targeted PCR to perform in a highly efficient mannerinvolves designing primers that are unlikely to hybridize with oneanother. The PCR probes, typically referred to as primers, are selectedby creating a thermodynamic model of potentially adverse interactionsbetween at least 500, at least 1,000, at least 5,000, at least 10,000,at least 20,000, at least 50,000, or at least 100,000 potential primerpairs, or unintended interactions between primers and sample DNA, andthen using the model to eliminate designs that are incompatible withother the designs in the pool. Another technique that allows highlymultiplexed targeted PCR to perform in a highly efficient manner isusing a partial or full nesting approach to the targeted PCR. Using oneor a combination of these approaches allows multiplexing of at least300, at least 800, at least 1,200, at least 4,000 or at least 10,000primers in a single pool with the resulting amplified DNA comprising amajority of DNA molecules that, when sequenced, will map to targetedloci. Using one or a combination of these approaches allows multiplexingof a large number of primers in a single pool with the resultingamplified DNA comprising greater than 50%, greater than 80%, greaterthan 90%, greater than 95%, greater than 98%, or greater than 99% DNAmolecules that map to targeted loci.

In an embodiment, a method disclosed herein yields a quantitativemeasure of the number of independent observations of each allele at apolymorphic locus. This is unlike most methods such as microarrays orqualitative PCR which provide information about the ratio of two allelesbut do not quantify the number of independent observations of eitherallele. With methods that provide quantitative information regarding thenumber of independent observations, only the ratio is utilized in ploidycalculations, while the quantitative information by itself is notuseful. To illustrate the importance of retaining information about thenumber of independent observations consider the sample locus with twoalleles, A and B. In a first experiment twenty A alleles and twenty Balleles are observed, in a second experiment 200 A alleles and 200 Balleles are observed. In both experiments the ratio (A/(A+B)) is equalto 0.5, however the second experiment conveys more information than thefirst about the certainty of the frequency of the A or B allele. Somemethods known in the prior art involve averaging or summing alleleratios (channel ratios) (i.e. x_(i)/y_(i)) from individual allele andanalyzes this ratio, either comparing it to a reference chromosome orusing a rule pertaining to how this ratio is expected to behave inparticular situations. No allele weighting is implied in such methodsknown in the art, where it is assumed that one can ensure about the sameamount of PCR product for each allele and that all the alleles shouldbehave the same way. Such a method has a number of disadvantages, andmore importantly, precludes the use a number of improvements that aredescribed elsewhere in this disclosure.

In an embodiment, a method disclosed herein explicitly models the allelefrequency distributions expected in disomy as well as a plurality ofallele frequency distributions that may be expected in cases of trisomyresulting from nondisjunction during meiosis I, nondisjunction duringmeiosis II, and/or nondisjunction during mitoisis early in fetaldevelopment. To illustrate why this is important, imagine a case wherethere were no crossovers: nondisjunction during meiosis I would result atrisomy in which two different homologs were inherited from one parent;in contrast, nondisjunction during meiosis II or during mitoisis earlyin fetal development would result in two copies of the same homolog fromone parent. Each scenario would result in different expected allelefrequencies at each polymorphic locus and also at all loci consideredjointly, due to genetic linkage. Crossovers, which result in theexchange of genetic material between homologs, make the inheritancepattern more complex; in an embodiment, the instant method accommodatesfor this by using recombination rate information in addition to thephysical distance between loci. In an embodiment, to enable improveddistinction between meiosis I nondisjunction and meiosis II or mitoticnondisjunction the instant method incorporate into the model anincreasing probability of crossover as the distance from the centromereincreases. Meiosis II and mitotic nondisjunction can distinguished bythe fact that mitotic nondisjunction typically results in identical ornearly identical copies of one homolog while the two homologs presentfollowing a meiosis II nondisjunction event often differ due to one ormore crossovers during gametogenesis.

In some embodiments, a method disclosed herein involves comparing theobserved allele measurements to theoretical hypotheses corresponding topossible fetal genetic aneuploidy, and does not involve a step ofquantitating a ratio of alleles at a heterozygous locus. Where thenumber of loci is lower than about 20, the ploidy determination madeusing a method comprising quantitating a ratio of alleles at aheterozygous locus and a ploidy determination made using a methodcomprising comparing the observed allele measurements to theoreticalallele distribution hypotheses corresponding to possible fetal geneticstates may give a similar result. However, where the number of loci isabove 50 these two methods is likely to give significantly differentresults; where the number of loci is above 400, above, 1,000 or above2,000 these two methods are very likely to give results that areincreasingly significantly different. These differences are due to thefact that a method that comprises quantitating a ratio of alleles at aheterozygous locus without measuring the magnitude of each alleleindependently and aggregating or averaging the ratios precludes the useof techniques including using a joint distribution model, performing alinkage analysis, using a binomial distribution model, and/or otheradvanced statistical techniques, whereas using a method comprisingcomparing the observed allele measurements to theoretical alleledistribution hypotheses corresponding to possible fetal genetic statesmay use these techniques which can substantially increase the accuracyof the determination.

In an embodiment, a method disclosed herein involves determining whetherthe distribution of observed allele measurements is indicative of aeuploid or an aneuploid fetus using a joint distribution model. The useof a joint distribution model is a different from and a significantimprovement over methods that determine heterozygosity rates by treatingpolymorphic loci independently in that the resultant determinations areof significantly higher accuracy. Without being bound by any particulartheory, it is believed that one reason they are of higher accuracy isthat the joint distribution model takes into account the linkage betweenSNPs, and likelihood of crossovers having occurred during the meiosisthat gave rise to the gametes that formed the embryo that grew into thefetus. The purpose of using the concept of linkage when creating theexpected distribution of allele measurements for one or more hypothesesis that it allows the creation of expected allele measurementsdistributions that correspond to reality considerably better than whenlinkage is not used. For example, imagine that there are two SNPs, 1 and2 located nearby one another, and the mother is A at SNP 1 and A at SNP2 on one homolog, and B at SNP 1 and B at SNP 2 on homolog two. If thefather is A for both SNPs on both homologs, and a B is measured for thefetus SNP 1, this indicates that homolog two has been inherited by thefetus, and therefore that there is a much higher likelihood of a B beingpresent on the fetus at SNP 2. A model that takes into account linkagewould predict this, while a model that does not take linkage intoaccount would not. Alternately, if a mother was AB at SNP 1 and AB atnearby SNP 2, then two hypotheses corresponding to maternal trisomy atthat location could be used—one involving a matching copy error(nondisjunction in meiosis II or mitosis in early fetal development),and one involving an unmatching copy error (nondisjunction in meiosisI). In the case of a matching copy error trisomy, if the fetus inheritedan AA from the mother at SNP 1, then the fetus is much more likely toinherit either an AA or BB from the mother at SNP 2, but not AB. In thecase of an unmatching copy error, the fetus would inherit an AB from themother at both SNPs. The allele distribution hypotheses made by a ploidycalling method that takes into account linkage would make thesepredictions, and therefore correspond to the actual allele measurementsto a considerably greater extent than a ploidy calling method that didnot take into account linkage. Note that a linkage approach is notpossible when using a method that relies on calculating allele ratiosand aggregating those allele ratios.

One reason that it is believed that ploidy determinations that use amethod that comprises comparing the observed allele measurements totheoretical hypotheses corresponding to possible fetal genetic statesare of higher accuracy is that when sequencing is used to measure thealleles, this method can glean more information from data from alleleswhere the total number of reads is low than other methods; for example,a method that relies on calculating and aggregating allele ratios wouldproduce disproportionately weighted stochastic noise. For example,imagine a case that involved measuring the alleles using sequencing, andwhere there was a set of loci where only five sequence reads weredetected for each locus. In an embodiment, for each of the alleles, thedata may be compared to the hypothesized allele distribution, andweighted according to the number of sequence reads; therefore the datafrom these measurements would be appropriately weighted and incorporatedinto the overall determination. This is in contrast to a method thatinvolved quantitating a ratio of alleles at a heterozygous locus, asthis method could only calculate ratios of 0%, 20%, 40%, 60%, 80% or100% as the possible allele ratios; none of these may be close toexpected allele ratios. In this latter case, the calculated allelerations would either have to be discarded due to insufficient reads orelse would have disproportionate weighting and introduce stochasticnoise into the determination, thereby decreasing the accuracy of thedetermination. In an embodiment, the individual allele measurements maybe treated as independent measurements, where the relationship betweenmeasurements made on alleles at the same locus is no different from therelationship between measurements made on alleles at different loci.

In an embodiment, a method disclosed herein involves determining whetherthe distribution of observed allele measurements is indicative of aeuploid or an aneuploid fetus without comparing any metrics to observedallele measurements on a reference chromosome that is expected to bedisomic (termed the RC method). This is a significant improvement overmethods, such as methods using shotgun sequencing which detectaneuploidy by evaluating the proportion of randomly sequenced fragmentsfrom a suspect chromosomes relative to one or more presumed disomicreference chromosome. This RC method yields incorrect results if thepresumed disomic reference chromosome is not actually disomic. This canoccur in cases where aneuploidy is more substantial than trisomy of asingle chromosome or where the fetus is triploid and all autosomes aretrisomic. In the case of a female triploid (69, XXX) fetus there are infact no disomic chromosomes at all. The method described herein does notrequire a reference chromosome and would be able to correctly identifytrisomic chromosomes in a female triploid fetus. For each chromosome,hypothesis, child fraction and noise level, a joint distribution modelmay be fit, without any of: reference chromosome data, an overall childfraction estimate, or a fixed reference hypothesis.

In an embodiment, a method disclosed herein demonstrates how observingallele distributions at polymorphic loci can be used to determine theploidy state of a fetus with greater accuracy than methods in the priorart. In an embodiment, the method uses the targeted sequencing to obtainmixed maternal-fetal genotypes and optionally mother and/or fathergenotypes at a plurality of SNPs to first establish the various expectedallele frequency distributions under the different hypotheses, and thenobserving the quantitative allele information obtained on thematernal-fetal mixture and evaluating which hypothesis fits the databest, where the genetic state corresponding to the hypothesis with thebest fit to the data is called as the correct genetic state. In anembodiment, a method disclosed herein also uses the degree of fit togenerate a confidence that the called genetic state is the correctgenetic state. In an embodiment, a method disclosed herein involvesusing algorithms that analyze the distribution of alleles found for locithat have different parental contexts, and comparing the observed alleledistributions to the expected allele distributions for different ploidystates for the different parental contexts (different parental genotypicpatterns). This is different from and an improvement over methods thatdo not use methods that enable the estimation of the number ofindependent instances of each allele at each locus in a mixedmaternal-fetal sample. In an embodiment, a method disclosed hereininvolves determining whether the distribution of observed allelemeasurements is indicative of a euploid or an aneuploid fetus usingobserved allelic distributions measured at loci where the mother isheterozygous. This is different from and an improvement over methodsthat do not use observed allelic distributions at loci where the motheris heterozygous because, in cases where the DNA is not preferentiallyenriched or is preferentially enriched for loci that are not known to behighly informative for that particular target individual, it allows theuse of about twice as much genetic measurement data from a set ofsequence data in the ploidy determination, resulting in a more accuratedetermination.

In an embodiment, a method disclosed herein uses a joint distributionmodel that assumes that the allele frequencies at each locus aremultinomial (and thus binomial when SNPs are biallelic) in nature. Insome embodiments the joint distribution model uses beta-binomialdistributions. When using a measuring technique, such as sequencing,provides a quantitative measure for each allele present at each locus,binomal model can be applied to each locus and the degree underlyingallele frequencies and the confidence in that frequency can beascertained. With methods known in the art that generate ploidy callsfrom allele ratios, or methods in which quantitative allele informationis discarded, the certainty in the observed ratio cannot be ascertained.The instant method is different from and an improvement over methodsthat calculate allele ratios and aggregate those ratios to make a ploidycall, since any method that involves calculating an allele ratio at aparticular locus, and then aggregating those ratios, necessarily assumesthat the measured intensities or counts that are indicative of theamount of DNA from any given allele or locus will be distributed in aGaussian fashion. The method disclosed herein does not involvecalculating allele ratios. In some embodiments, a method disclosedherein may involve incorporating the number of observations of eachallele at a plurality of loci into a model. In some embodiments, amethod disclosed herein may involve calculating the expecteddistributions themselves, allowing the use of a joint binomialdistribution model which may be more accurate than any model thatassumes a Gaussian distribution of allele measurements. The likelihoodthat the binomial distribution model is significantly more accurate thanthe Gaussian distribution increases as the number of loci increases. Forexample, when fewer than 20 loci are interrogated, the likelihood thatthe binomial distribution model is significantly better is low. However,when more than 100, or especially more than 400, or especially more than1,000, or especially more than 2,000 loci are used, the binomialdistribution model will have a very high likelihood of beingsignificantly more accurate than the Gaussian distribution model,thereby resulting in a more accurate ploidy determination. Thelikelihood that the binomial distribution model is significantly moreaccurate than the Gaussian distribution also increases as the number ofobservations at each locus increases. For example, when fewer than 10distinct sequences are observed at each locus are observed, thelikelihood that the binomial distribution model is significantly betteris low. However, when more than 50 sequence reads, or especially morethan 100 sequence reads, or especially more than 200 sequence reads, orespecially more than 300 sequence reads are used for each locus, thebinomial distribution model will have a very high likelihood of beingsignificantly more accurate than the Gaussian distribution model,thereby resulting in a more accurate ploidy determination.

In an embodiment, a method disclosed herein uses sequencing to measurethe number of instances of each allele at each locus in a DNA sample.Each sequencing read may be mapped to a specific locus and treated as abinary sequence read; alternately, the probability of the identity ofthe read and/or the mapping may be incorporated as part of the sequenceread, resulting in a probabilistic sequence read, that is, the probablewhole or fractional number of sequence reads that map to a given loci.Using the binary counts or probability of counts it is possible to use abinomial distribution for each set of measurements, allowing aconfidence interval to be calculated around the number of counts. Thisability to use the binomial distribution allows for more accurate ploidyestimations and more precise confidence intervals to be calculated. Thisis different from and an improvement over methods that use intensitiesto measure the amount of an allele present, for example methods that usemicroarrays, or methods that make measurements using fluorescencereaders to measure the intensity of fluorescently tagged DNA inelectrophoretic bands.

In an embodiment, a method disclosed herein uses aspects of the presentset of data to determine parameters for the estimated allele frequencydistribution for that set of data. This is an improvement over methodsthat utilize training set of data or prior sets of data to setparameters for the present expected allele frequency distributions, orpossibly expected allele ratios. This is because there are differentsets of conditions involved in the collection and measurement of everygenetic sample, and thus a method that uses data from the instant set ofdata to determine the parameters for the joint distribution model thatis to be used in the ploidy determination for that sample will tend tobe more accurate.

In an embodiment, a method disclosed herein involves determining whetherthe distribution of observed allele measurements is indicative of aeuploid or an aneuploid fetus using a maximum likelihood technique. Theuse of a maximum likelihood technique is different from and asignificant improvement over methods that use single hypothesisrejection technique in that the resultant determinations will be madewith significantly higher accuracy. One reason is that single hypothesisrejection techniques set cut off thresholds based on only onemeasurement distribution rather than two, meaning that the thresholdsare usually not optimal. Another reason is that the maximum likelihoodtechnique allows the optimization of the cut off threshold for eachindividual sample instead of determining a cut off threshold to be usedfor all samples regardless of the particular characteristics of eachindividual sample. Another reason is that the use of a maximumlikelihood technique allows the calculation of a confidence for eachploidy call. The ability to make a confidence calculation for each callallows a practitioner to know which calls are accurate, and which aremore likely to be wrong. In some embodiments, a wide variety of methodsmay be combined with a maximum likelihood estimation technique toenhance the accuracy of the ploidy calls. In an embodiment, the maximumlikelihood technique may be used in combination with the methoddescribed in U.S. Pat. No. 7,888,017. In an embodiment, the maximumlikelihood technique may be used in combination with the method of usingtargeted PCR amplification to amplify the DNA in the mixed samplefollowed by sequencing and analysis using a read counting method such asused by TANDEM DIAGNOSTICS, as presented at the International Congressof Human Genetics 2011, in Montreal in October 2011. In an embodiment, amethod disclosed herein involves estimating the fetal fraction of DNA inthe mixed sample and using that estimation to calculate both the ploidycall and the confidence of the ploidy call. Note that this is bothdifferent and distinct from methods that use estimated fetal fraction asa screen for sufficient fetal fraction, followed by a ploidy call madeusing a single hypothesis rejection technique that does not take intoaccount the fetal fraction nor does it produce a confidence calculationfor the call.

In an embodiment, a method disclosed herein takes into account thetendency for the data to be noisy and contain errors by attaching aprobability to each measurement. The use of maximum likelihoodtechniques to choose the correct hypothesis from the set of hypothesesthat were made using the measurement data with attached probabilisticestimates makes it more likely that the incorrect measurements will bediscounted, and the correct measurements will be used in thecalculations that lead to the ploidy call. To be more precise, thismethod systematically reduces the influence of data that is incorrectlymeasured on the ploidy determination. This is an improvement overmethods where all data is assumed to be equally correct or methods whereoutlying data is arbitrarily excluded from calculations leading to aploidy call. Existing methods using channel ratio measurements claim toextend the method to multiple SNPs by averaging individual SNP channelratios. Not weighting individual SNPs by expected measurement variancebased on the SNP quality and observed depth of read reduces the accuracyof the resulting statistic, resulting in a reduction of the accuracy ofthe ploidy call significantly, especially in borderline cases.

In an embodiment, a method disclosed herein does not presuppose theknowledge of which SNPs or other polymorphic loci are heterozygous onthe fetus. This method allows a ploidy call to be made in cases wherepaternal genotypic information is not available. This is an improvementover methods where the knowledge of which SNPs are heterozygous must beknown ahead of time in order to appropriately select loci to target, orto interpret the genetic measurements made on the mixed fetal/maternalDNA sample.

The methods described herein are particularly advantageous when used onsamples where a small amount of DNA is available, or where the percentof fetal DNA is low. This is due to the correspondingly higher alleledropout rate that occurs when only a small amount of DNA is availableand/or the correspondingly higher fetal allele dropout rate when thepercent of fetal DNA is low in a mixed sample of fetal and maternal DNA.A high allele dropout rate, meaning that a large percentage of thealleles were not measured for the target individual, results in poorlyaccurate fetal fractions calculations, and poorly accurate ploidydeterminations. Since methods disclosed herein may use a jointdistribution model that takes into account the linkage in inheritancepatterns between SNPs, significantly more accurate ploidy determinationsmay be made. The methods described herein allow for an accurate ploidydetermination to be made when the percent of molecules of DNA that arefetal in the mixture is less than 40%, less than 30%, less than 20%,less than 10%, less than 8%, and even less than 6%.

In an embodiment, it is possible to determine the ploidy state of anindividual based on measurements when that individual's DNA is mixedwith DNA of a related individual. In an embodiment, the mixture of DNAis the free floating DNA found in maternal plasma, which may include DNAfrom the mother, with known karyotype and known genotype, and which maybe mixed with DNA of the fetus, with unknown karyotype and unknowngenotype. It is possible to use the known genotypic information from oneor both parents to predict a plurality of potential genetic states ofthe DNA in the mixed sample for different ploidy states, differentchromosome contributions from each parent to the fetus, and optionally,different fetal DNA fractions in the mixture. Each potential compositionmay be referred to as a hypothesis. The ploidy state of the fetus canthen be determined by looking at the actual measurements, anddetermining which potential compositions are most likely given theobserved data.

In some embodiments, a method disclosed herein could be used insituations where there is a very small amount of DNA present, such as inin vitro fertilization, or in forensic situations, where one or a fewcells are available (typically less than ten cells, less than twentycells or less than 40 cells.) In these embodiments, a method disclosedherein serves to make ploidy calls from a small amount of DNA that isnot contaminated by other DNA, but where the ploidy calling verydifficult the small amount of DNA. In some embodiments, a methoddisclosed herein could be used in situations where the target DNA iscontaminated with DNA of another individual, for example in maternalblood in the context of prenatal diagnosis, paternity testing, orproducts of conception testing. Some other situations where thesemethods would be particularly advantageous would be in the case ofcancer testing where only one or a small number of cells were presentamong a larger amount of normal cells. The genetic measurements used aspart of these methods could be made on any sample comprising DNA or RNA,for example but not limited to: blood, plasma, body fluids, urine, hair,tears, saliva, tissue, skin, fingernails, blastomeres, embryos, amnioticfluid, chorionic villus samples, feces, bile, lymph, cervical mucus,semen, or other cells or materials comprising nucleic acids. In anembodiment, a method disclosed herein could be run with nucleic aciddetection methods such as sequencing, microarrays, qPCR, digital PCR, orother methods used to measure nucleic acids. If for some reason it werefound to be desirable, the ratios of the allele count probabilities at alocus could be calculated, and the allele ratios could be used todetermine ploidy state in combination with some of the methods describedherein, provided the methods are compatible. In some embodiments, amethod disclosed herein involves calculating, on a computer, alleleratios at the plurality of polymorphic loci from the DNA measurementsmade on the processed samples. In some embodiments, a method disclosedherein involves calculating, on a computer, allele ratios at theplurality of polymorphic loci from the DNA measurements made on theprocessed samples along with any combination of other improvementsdescribed in this disclosure.

Further discussion of the points above may be found elsewhere in thisdocument.

Non-Invasive Prenatal Diagnosis (NPD)

The process of non-invasive prenatal diagnosis involves a number ofsteps. Some of the steps may include: (1) obtaining the genetic materialfrom the fetus; (2) enriching the genetic material of the fetus that maybe in a mixed sample, ex vivo; (3) amplifying the genetic material, exvivo; (4) preferentially enriching specific loci in the geneticmaterial, ex vivo; (5) measuring the genetic material, ex vivo; and (6)analyzing the genotypic data, on a computer, and ex vivo. Methods toreduce to practice these six and other relevant steps are describedherein. At least some of the method steps are not directly applied onthe body. In an embodiment, the present disclosure relates to methods oftreatment and diagnosis applied to tissue and other biological materialsisolated and separated from the body. At least some of the method stepsare executed on a computer.

Some embodiments of the present disclosure allow a clinician todetermine the genetic state of a fetus that is gestating in a mother ina non-invasive manner such that the health of the baby is not put atrisk by the collection of the genetic material of the fetus, and thatthe mother is not required to undergo an invasive procedure. Moreover,in certain aspects, the present disclosure allows the fetal geneticstate to be determined with high accuracy, significantly greateraccuracy than, for example, the non-invasive maternal serum analytebased screens, such as the triple test, that are in wide use in prenatalcare.

The high accuracy of the methods disclosed herein is a result of aninformatics approach to analysis of the genotype data, as describedherein. Modern technological advances have resulted in the ability tomeasure large amounts of genetic information from a genetic sample usingsuch methods as high throughput sequencing and genotyping arrays. Themethods disclosed herein allow a clinician to take greater advantage ofthe large amounts of data available, and make a more accurate diagnosisof the fetal genetic state. The details of a number of embodiments aregiven below. Different embodiments may involve different combinations ofthe aforementioned steps. Various combinations of the differentembodiments of the different steps may be used interchangeably.

In an embodiment, a blood sample is taken from a pregnant mother, andthe free floating DNA in the plasma of the mother's blood, whichcontains a mixture of both DNA of maternal origin, and DNA of fetalorigin, is isolated and used to determine the ploidy status of thefetus. In an embodiment, a method disclosed herein involves preferentialenrichment of those DNA sequences in a mixture of DNA that correspond topolymorphic alleles in a way that the allele ratios and/or alleledistributions remain mostly consistent upon enrichment. In anembodiment, a method disclosed herein involves the highly efficienttargeted PCR based amplification such that a very high percentage of theresulting molecules correspond to targeted loci. In an embodiment, amethod disclosed herein involves sequencing a mixture of DNA thatcontains both DNA of maternal origin, and DNA of fetal origin. In anembodiment, a method disclosed herein involves using measured alleledistributions to determine the ploidy state of a fetus that is gestatingin a mother. In an embodiment, a method disclosed herein involvesreporting the determined ploidy state to a clinician. In an embodiment,a method disclosed herein involves taking a clinical action, forexample, performing follow up invasive testing such as chorionic villussampling or amniocentesis, preparing for the birth of a trisomicindividual or an elective termination of a trisomic fetus.

This application makes reference to U.S. Utility application Ser. No.11/603,406, filed Nov. 28, 2006 (US Publication No.: 20070184467); U.S.Utility application Ser. No. 12/076,348, filed Mar. 17, 2008 (USPublication No.: 20080243398); PCT Utility Application Serial No.PCT/US09/52730, filed Aug. 4, 2009 (PCT Publication No.:WO/2010/017214); PCT Utility Application Serial No. PCT/US10/050824,filed Sep. 30, 2010 (PCT Publication No.: WO/2011/041485), and U.S.Utility application Ser. No. 13/110,685, filed May 18, 2011. Some of thevocabulary used in this filing may have its antecedents in thesereferences. Some of the concepts described herein may be betterunderstood in light of the concepts found in these references.

Screening Maternal Blood Comprising Free Floating Fetal DNA

The methods described herein may be used to help determine the genotypeof a child, fetus, or other target individual where the genetic materialof the target is found in the presence of a quantity of other geneticmaterial. In some embodiments the genotype may refer to the ploidy stateof one or a plurality of chromosomes, it may refer to one or a pluralityof disease linked alleles, or some combination thereof. In thisdisclosure, the discussion focuses on determining the genetic state of afetus where the fetal DNA is found in maternal blood, but this exampleis not meant to limit to possible contexts that this method may beapplied to. In addition, the method may be applicable in cases where theamount of target DNA is in any proportion with the non-target DNA; forexample, the target DNA could make up anywhere between 0.000001 and99.999999% of the DNA present. In addition, the non-target DNA does notnecessarily need to be from one individual, or even from a relatedindividual, as long as genetic data from some or all of the relevantnon-target individual(s) is known. In an embodiment, a method disclosedherein can be used to determine genotypic data of a fetus from maternalblood that contains fetal DNA. It may also be used in a case where thereare multiple fetuses in the uterus of a pregnant woman, or where othercontaminating DNA may be present in the sample, for example from otheralready born siblings.

This technique may make use of the phenomenon of fetal blood cellsgaining access to maternal circulation through the placental villi.Ordinarily, only a very small number of fetal cells enter the maternalcirculation in this fashion (not enough to produce a positiveKleihauer-Betke test for fetal-maternal hemorrhage). The fetal cells canbe sorted out and analyzed by a variety of techniques to look forparticular DNA sequences, but without the risks that invasive proceduresinherently have. This technique may also make use of the phenomenon offree floating fetal DNA gaining access to maternal circulation by DNArelease following apoptosis of placental tissue where the placentaltissue in question contains DNA of the same genotype as the fetus. Thefree floating DNA found in maternal plasma has been shown to containfetal DNA in proportions as high as 30-40% fetal DNA.

In an embodiment, blood may be drawn from a pregnant woman. Research hasshown that maternal blood may contain a small amount of free floatingDNA from the fetus, in addition to free floating DNA of maternal origin.In addition, there also may be enucleated fetal blood cells comprisingDNA of fetal origin, in addition to many blood cells of maternal origin,which typically do not contain nuclear DNA. There are many methods knowin the art to isolate fetal DNA, or create fractions enriched in fetalDNA. For example, chromatography has been show to create certainfractions that are enriched in fetal DNA.

Once the sample of maternal blood, plasma, or other fluid, drawn in arelatively non-invasive manner, and that contains an amount of fetalDNA, either cellular or free floating, either enriched in its proportionto the maternal DNA, or in its original ratio, is in hand, one maygenotype the DNA found in said sample. In some embodiments, the bloodmay be drawn using a needle to withdraw blood from a vein, for example,the basilica vein. The method described herein can be used to determinegenotypic data of the fetus. For example, it can be used to determinethe ploidy state at one or more chromosomes, it can be used to determinethe identity of one or a set of SNPs, including insertions, deletions,and translocations. It can be used to determine one or more haplotypes,including the parent of origin of one or more genotypic features.

Note that this method will work with any nucleic acids that can be usedfor any genotyping and/or sequencing methods, such as the ILLUMINAINFINIUM ARRAY platform, AFFYMETRIX GENECHIP, ILLUMINA GENOME ANALYZER,or LIFE TECHNOLGIES' SOLID SYSTEM. This includes extracted free-floatingDNA from plasma or amplifications (e.g. whole genome amplification, PCR)of the same; genomic DNA from other cell types (e.g. human lymphocytesfrom whole blood) or amplifications of the same. For preparation of theDNA, any extraction or purification method that generates genomic DNAsuitable for the one of these platforms will work as well. This methodcould work equally well with samples of RNA. In an embodiment, storageof the samples may be done in a way that will minimize degradation (e.g.below freezing, at about −20 C, or at a lower temperature).

Parental Support

Some embodiments may be used in combination with the PARENTAL SUPPORT™(PS) method, embodiments of which are described in U.S. application Ser.No. 11/603,406 (US Publication No.: 20070184467), U.S. application Ser.No. 12/076,348 (US Publication No.: 20080243398), U.S. application Ser.No. 13/110,685, PCT Application PCT/US09/52730 (PCT Publication No.:WO/2010/017214), and PCT Application No. PCT/US10/050824 (PCTPublication No.: WO/2011/041485) which are incorporated herein byreference in their entirety. PARENTAL SUPPORT™ is an informatics basedapproach that can be used to analyze genetic data. In some embodiments,the methods disclosed herein may be considered as part of the PARENTALSUPPORT™ method. In some embodiments, The PARENTAL SUPPORT™ method is acollection of methods that may be used to determine the genetic data ofa target individual, with high accuracy, of one or a small number ofcells from that individual, or of a mixture of DNA consisting of DNAfrom the target individual and DNA from one or a plurality of otherindividuals, specifically to determine disease-related alleles, otheralleles of interest, and/or the ploidy state of one or a plurality ofchromosomes in the target individual. PARENTAL SUPPORT™ may refer to anyof these methods. PARENTAL SUPPORT™ is an example of an informaticsbased method.

The PARENTAL SUPPORT™ method makes use of known parental genetic data,i.e. haplotypic and/or diploid genetic data of the mother and/or thefather, together with the knowledge of the mechanism of meiosis and theimperfect measurement of the target DNA, and possibly of one or morerelated individuals, along with population based crossover frequencies,in order to reconstruct, in silico, the genotype at a plurality ofalleles, and/or the ploidy state of an embryo or of any target cell(s),and the target DNA at the location of key loci with a high degree ofconfidence. The PARENTAL SUPPORT™ method can reconstruct not only singlenucleotide polymorphisms (SNPs) that were measured poorly, but alsoinsertions and deletions, and SNPs or whole regions of DNA that were notmeasured at all. Furthermore, the PARENTAL SUPPORT™ method can bothmeasure multiple disease-linked loci as well as screen for aneuploidy,from a single cell. In some embodiments, the PARENTAL SUPPORT™ methodmay be used to characterize one or more cells from embryos biopsiedduring an IVF cycle to determine the genetic condition of the one ormore cells.

The PARENTAL SUPPORT™ method allows the cleaning of noisy genetic data.This may be done by inferring the correct genetic alleles in the targetgenome (embryo) using the genotype of related individuals (parents) as areference. PARENTAL SUPPORT may be particularly relevant where only asmall quantity of genetic material is available (e.g. PGD) and wheredirect measurements of the genotypes are inherently noisy due to thelimited amounts of genetic material. PARENTAL SUPPORT™ may beparticularly relevant where only a small fraction of the geneticmaterial available is from the target individual (e.g. NPD) and wheredirect measurements of the genotypes are inherently noisy due to thecontaminating DNA signal from another individual. The PARENTAL SUPPORT™method is able to reconstruct highly accurate ordered diploid allelesequences on the embryo, together with copy number of chromosomessegments, even though the conventional, unordered diploid measurementsmay be characterized by high rates of allele dropouts, drop-ins,variable amplification biases and other errors. The method may employboth an underlying genetic model and an underlying model of measurementerror. The genetic model may determine both allele probabilities at eachSNP and crossover probabilities between SNPs. Allele probabilities maybe modeled at each SNP based on data obtained from the parents and modelcrossover probabilities between SNPs based on data obtained from theHapMap database, as developed by the International HapMap Project. Giventhe proper underlying genetic model and measurement error model, maximuma posteriori (MAP) estimation may be used, with modifications forcomputationally efficiency, to estimate the correct, ordered allelevalues at each SNP in the embryo.

The techniques outlined above, in some cases, are able to determine thegenotype of an individual given a very small amount of DNA originatingfrom that individual. This could be the DNA from one or a small numberof cells, or it could be from the small amount of fetal DNA found inmaternal blood.

Definitions

-   Single Nucleotide Polymorphism (SNP) refers to a single nucleotide    that may differ between the genomes of two members of the same    species. The usage of the term should not imply any limit on the    frequency with which each variant occurs.-   Sequence refers to a DNA sequence or a genetic sequence. It may    refer to the primary, physical structure of the DNA molecule or    strand in an individual. It may refer to the sequence of nucleotides    found in that DNA molecule, or the complementary strand to the DNA    molecule. It may refer to the information contained in the DNA    molecule as its representation in silico.-   Locus refers to a particular region of interest on the DNA of an    individual, which may refer to a SNP, the site of a possible    insertion or deletion, or the site of some other relevant genetic    variation. Disease-linked SNPs may also refer to disease-linked    loci.-   Polymorphic Allele, also “Polymorphic Locus,” refers to an allele or    locus where the genotype varies between individuals within a given    species. Some examples of polymorphic alleles include single    nucleotide polymorphisms, short tandem repeats, deletions,    duplications, and inversions.-   Polymorphic Site refers to the specific nucleotides found in a    polymorphic region that vary between individuals.-   Allele refers to the genes that occupy a particular locus.-   Genetic Data also “Genotypic Data” refers to the data describing    aspects of the genome of one or more individuals. It may refer to    one or a set of loci, partial or entire sequences, partial or entire    chromosomes, or the entire genome. It may refer to the identity of    one or a plurality of nucleotides; it may refer to a set of    sequential nucleotides, or nucleotides from different locations in    the genome, or a combination thereof. Genotypic data is typically in    silico, however, it is also possible to consider physical    nucleotides in a sequence as chemically encoded genetic data.    Genotypic Data may be said to be “on,” “of,” “at,” “from” or “on”    the individual(s). Genotypic Data may refer to output measurements    from a genotyping platform where those measurements are made on    genetic material.-   Genetic Material also “Genetic Sample” refers to physical matter,    such as tissue or blood, from one or more individuals comprising DNA    or RNA-   Noisy Genetic Data refers to genetic data with any of the following:    allele dropouts, uncertain base pair measurements, incorrect base    pair measurements, missing base pair measurements, uncertain    measurements of insertions or deletions, uncertain measurements of    chromosome segment copy numbers, spurious signals, missing    measurements, other errors, or combinations thereof.-   Confidence refers to the statistical likelihood that the called SNP,    allele, set of alleles, ploidy call, or determined number of    chromosome segment copies correctly represents the real genetic    state of the individual.-   Ploidy Calling, also “Chromosome Copy Number Calling,” or “Copy    Number Calling” (CNC), may refer to the act of determining the    quantity and/or chromosomal identity of one or more chromosomes    present in a cell.-   Aneuploidy refers to the state where the wrong number of chromosomes    is present in a cell. In the case of a somatic human cell it may    refer to the case where a cell does not contain 22 pairs of    autosomal chromosomes and one pair of sex chromosomes. In the case    of a human gamete, it may refer to the case where a cell does not    contain one of each of the 23 chromosomes. In the case of a single    chromosome type, it may refer to the case where more or less than    two homologous but non-identical chromosome copies are present, or    where there are two chromosome copies present that originate from    the same parent.-   Ploidy State refers to the quantity and/or chromosomal identity of    one or more chromosomes types in a cell.-   Chromosome may refer to a single chromosome copy, meaning a single    molecule of DNA of which there are 46 in a normal somatic cell; an    example is ‘the maternally derived chromosome 18’. Chromosome may    also refer to a chromosome type, of which there are 23 in a normal    human somatic cell; an example is ‘chromosome 18’.-   Chromosomal Identity may refer to the referent chromosome number,    i.e. the chromosome type. Normal humans have 22 types of numbered    autosomal chromosome types, and two types of sex chromosomes. It may    also refer to the parental origin of the chromosome. It may also    refer to a specific chromosome inherited from the parent. It may    also refer to other identifying features of a chromosome.-   The State of the Genetic Material or simply “Genetic State” may    refer to the identity of a set of SNPs on the DNA, to the phased    haplotypes of the genetic material, and to the sequence of the DNA,    including insertions, deletions, repeats and mutations. It may also    refer to the ploidy state of one or more chromosomes, chromosomal    segments, or set of chromosomal segments.-   Allelic Data refers to a set of genotypic data concerning a set of    one or more alleles. It may refer to the phased, haplotypic data. It    may refer to SNP identities, and it may refer to the sequence data    of the DNA, including insertions, deletions, repeats and mutations.    It may include the parental origin of each allele.-   Allelic State refers to the actual state of the genes in a set of    one or more alleles. It may refer to the actual state of the genes    described by the allelic data.-   Allelic Ratio or allele ratio, refers to the ratio between the    amount of each allele at a locus that is present in a sample or in    an individual. When the sample was measured by sequencing, the    allelic ratio may refer to the ratio of sequence reads that map to    each allele at the locus. When the sample was measured by an    intensity based measurement method, the allele ratio may refer to    the ratio of the amounts of each allele present at that locus as    estimated by the measurement method.-   Allele Count refers to the number of sequences that map to a    particular locus, and if that locus is polymorphic, it refers to the    number of sequences that map to each of the alleles. If each allele    is counted in a binary fashion, then the allele count will be whole    number. If the alleles are counted probabilistically, then the    allele count can be a fractional number.-   Allele Count Probability refers to the number of sequences that are    likely to map to a particular locus or a set of alleles at a    polymorphic locus, combined with the probability of the mapping.    Note that allele counts are equivalent to allele count probabilities    where the probability of the mapping for each counted sequence is    binary (zero or one). In some embodiments, the allele count    probabilities may be binary. In some embodiments, the allele count    probabilities may be set to be equal to the DNA measurements.-   Allelic Distribution, or ‘allele count distribution’ refers to the    relative amount of each allele that is present for each locus in a    set of loci. An allelic distribution can refer to an individual, to    a sample, or to a set of measurements made on a sample. In the    context of sequencing, the allelic distribution refers to the number    or probable number of reads that map to a particular allele for each    allele in a set of polymorphic loci. The allele measurements may be    treated probabilistically, that is, the likelihood that a given    allele is present for a give sequence read is a fraction between 0    and 1, or they may be treated in a binary fashion, that is, any    given read is considered to be exactly zero or one copies of a    particular allele.-   Allelic Distribution Pattern refers to a set of different allele    distributions for different parental contexts. Certain allelic    distribution patterns may be indicative of certain ploidy states.-   Allelic Bias refers to the degree to which the measured ratio of    alleles at a heterozygous locus is different to the ratio that was    present in the original sample of DNA. The degree of allelic bias at    a particular locus is equal to the observed allelic ratio at that    locus, as measured, divided by the ratio of alleles in the original    DNA sample at that locus. Allelic bias may be defined to be greater    than one, such that if the calculation of the degree of allelic bias    returns a value, x, that is less than 1, then the degree of allelic    bias may be restated as 1/x. Allelic bias maybe due to amplification    bias, purification bias, or some other phenomenon that affects    different alleles differently.-   Primer, also “PCR probe” refers to a single DNA molecule (a DNA    oligomer) or a collection of DNA molecules (DNA oligomers) where the    DNA molecules are identical, or nearly so, and where the primer    contains a region that is designed to hybridize to a targeted    polymorphic locus, and m contain a priming sequence designed to    allow PCR amplification. A primer may also contain a molecular    barcode. A primer may contain a random region that differs for each    individual molecule.-   Hybrid Capture Probe refers to any nucleic acid sequence, possibly    modified, that is generated by various methods such as PCR or direct    synthesis and intended to be complementary to one strand of a    specific target DNA sequence in a sample. The exogenous hybrid    capture probes may be added to a prepared sample and hybridized    through a denature-reannealing process to form duplexes of    exogenous-endogenous fragments. These duplexes may then be    physically separated from the sample by various means.-   Sequence Read refers to data representing a sequence of nucleotide    bases that were measured using a clonal sequencing method. Clonal    sequencing may produce sequence data representing single, or clones,    or clusters of one original DNA molecule. A sequence read may also    have associated quality score at each base position of the sequence    indicating the probability that nucleotide has been called    correctly.-   Mapping a sequence read is the process of determining a sequence    read's location of origin in the genome sequence of a particular    organism. The location of origin of sequence reads is based on    similarity of nucleotide sequence of the read and the genome    sequence.-   Matched Copy Error, also “Matching Chromosome Aneuploidy” (MCA),    refers to a state of aneuploidy where one cell contains two    identical or nearly identical chromosomes. This type of aneuploidy    may arise during the formation of the gametes in meiosis, and may be    referred to as a meiotic non-disjunction error. This type of error    may arise in mitosis. Matching trisomy may refer to the case where    three copies of a given chromosome are present in an individual and    two of the copies are identical.-   Unmatched Copy Error, also “Unique Chromosome Aneuploidy” (UCA),    refers to a state of aneuploidy where one cell contains two    chromosomes that are from the same parent, and that may be    homologous but not identical. This type of aneuploidy may arise    during meiosis, and may be referred to as a meiotic error.    Unmatching trisomy may refer to the case where three copies of a    given chromosome are present in an individual and two of the copies    are from the same parent, and are homologous, but are not identical.    Note that unmatching trisomy may refer to the case where two    homologous chromosomes from one parent are present, and where some    segments of the chromosomes are identical while other segments are    merely homologous.-   Homologous Chromosomes refers to chromosome copies that contain the    same set of genes that normally pair up during meiosis.-   Identical Chromosomes refers to chromosome copies that contain the    same set of genes, and for each gene they have the same set of    alleles that are identical, or nearly identical.-   Allele Drop Out (ADO) refers to the situation where at least one of    the base pairs in a set of base pairs from homologous chromosomes at    a given allele is not detected.-   Locus Drop Out (LDO) refers to the situation where both base pairs    in a set of base pairs from homologous chromosomes at a given allele    are not detected.-   Homozygous refers to having similar alleles as corresponding    chromosomal loci.-   Heterozygous refers to having dissimilar alleles as corresponding    chromosomal loci.-   Heterozygosity Rate refers to the rate of individuals in the    population having heterozygous alleles at a given locus. The    heterozygosity rate may also refer to the expected or measured ratio    of alleles, at a given locus in an individual, or a sample of DNA.-   Highly Informative Single Nucleotide Polymorphism (HISNP) refers to    a SNP where the fetus has an allele that is not present in the    mother's genotype.-   Chromosomal Region refers to a segment of a chromosome, or a full    chromosome.-   Segment of a Chromosome refers to a section of a chromosome that can    range in size from one base pair to the entire chromosome.-   Chromosome refers to either a full chromosome, or a segment or    section of a chromosome.-   Copies refers to the number of copies of a chromosome segment. It    may refer to identical copies, or to non-identical, homologous    copies of a chromosome segment wherein the different copies of the    chromosome segment contain a substantially similar set of loci, and    where one or more of the alleles are different. Note that in some    cases of aneuploidy, such as the M2 copy error, it is possible to    have some copies of the given chromosome segment that are identical    as well as some copies of the same chromosome segment that are not    identical.-   Haplotype refers to a combination of alleles at multiple loci that    are typically inherited together on the same chromosome. Haplotype    may refer to as few as two loci or to an entire chromosome depending    on the number of recombination events that have occurred between a    given set of loci. Haplotype can also refer to a set of single    nucleotide polymorphisms (SNPs) on a single chromatid that are    statistically associated.-   Haplotypic Data, also “Phased Data” or “Ordered Genetic Data,”    refers to data from a single chromosome in a diploid or polyploid    genome, i.e., either the segregated maternal or paternal copy of a    chromosome in a diploid genome.-   Phasing refers to the act of determining the haplotypic genetic data    of an individual given unordered, diploid (or polyploidy) genetic    data. It may refer to the act of determining which of two genes at    an allele, for a set of alleles found on one chromosome, are    associated with each of the two homologous chromosomes in an    individual.-   Phased Data refers to genetic data where one or more haplotypes have    been determined.-   Hypothesis refers to a possible ploidy state at a given set of    chromosomes, or a set of possible allelic states at a given set of    loci. The set of possibilities may comprise one or more elements.-   Copy Number Hypothesis, also “Ploidy State Hypothesis,” refers to a    hypothesis concerning the number of copies of a chromosome in an    individual. It may also refer to a hypothesis concerning the    identity of each of the chromosomes, including the parent of origin    of each chromosome, and which of the parent's two chromosomes are    present in the individual. It may also refer to a hypothesis    concerning which chromosomes, or chromosome segments, if any, from a    related individual correspond genetically to a given chromosome from    an individual.-   Target Individual refers to the individual whose genetic state is    being determined. In some embodiments, only a limited amount of DNA    is available from the target individual. In some embodiments, the    target individual is a fetus. In some embodiments, there may be more    than one target individual. In some embodiments, each fetus that    originated from a pair of parents may be considered to be target    individuals. In some embodiments, the genetic data that is being    determined is one or a set of allele calls. In some embodiments, the    genetic data that is being determined is a ploidy call.-   Related Individual refers to any individual who is genetically    related to, and thus shares haplotype blocks with, the target    individual. In one context, the related individual may be a genetic    parent of the target individual, or any genetic material derived    from a parent, such as a sperm, a polar body, an embryo, a fetus, or    a child. It may also refer to a sibling, parent or a grandparent.-   Sibling refers to any individual whose genetic parents are the same    as the individual in question. In some embodiments, it may refer to    a born child, an embryo, or a fetus, or one or more cells    originating from a born child, an embryo, or a fetus. A sibling may    also refer to a haploid individual that originates from one of the    parents, such as a sperm, a polar body, or any other set of    haplotypic genetic matter. An individual may be considered to be a    sibling of itself.-   Fetal refers to “of the fetus,” or “of the region of the placenta    that is genetically similar to the fetus”. In a pregnant woman, some    portion of the placenta is genetically similar to the fetus, and the    free floating fetal DNA found in maternal blood may have originated    from the portion of the placenta with a genotype that matches the    fetus. Note that the genetic information in half of the chromosomes    in a fetus is inherited from the mother of the fetus. In some    embodiments, the DNA from these maternally inherited chromosomes    that came from a fetal cell is considered to be “of fetal origin,”    not “of maternal origin.”-   DNA of Fetal Origin refers to DNA that was originally part of a cell    whose genotype was essentially equivalent to that of the fetus.-   DNA of Maternal Origin refers to DNA that was originally part of a    cell whose genotype was essentially equivalent to that of the    mother.-   Child may refer to an embryo, a blastomere, or a fetus. Note that in    the presently disclosed embodiments, the concepts described apply    equally well to individuals who are a born child, a fetus, an embryo    or a set of cells therefrom. The use of the term child may simply be    meant to connote that the individual referred to as the child is the    genetic offspring of the parents.-   Parent refers to the genetic mother or father of an individual. An    individual typically has two parents, a mother and a father, though    this may not necessarily be the case such as in genetic or    chromosomal chimerism. A parent may be considered to be an    individual.-   Parental Context refers to the genetic state of a given SNP, on each    of the two relevant chromosomes for one or both of the two parents    of the target.-   Develop As Desired, also “Develop Normally,” refers to a viable    embryo implanting in a uterus and resulting in a pregnancy, and/or    to a pregnancy continuing and resulting in a live birth, and/or to a    born child being free of chromosomal abnormalities, and/or to a born    child being free of other undesired genetic conditions such as    disease-linked genes. The term “develop as desired” is meant to    encompass anything that may be desired by parents or healthcare    facilitators. In some cases, “develop as desired” may refer to an    unviable or viable embryo that is useful for medical research or    other purposes.-   Insertion into a Uterus refers to the process of transferring an    embryo into the uterine cavity in the context of in vitro    fertilization.-   Maternal Plasma refers to the plasma portion of the blood from a    female who is pregnant.-   Clinical Decision refers to any decision to take or not take an    action that has an outcome that affects the health or survival of an    individual. In the context of prenatal diagnosis, a clinical    decision may refer to a decision to abort or not abort a fetus. A    clinical decision may also refer to a decision to conduct further    testing, to take actions to mitigate an undesirable phenotype, or to    take actions to prepare for the birth of a child with abnormalities.-   Diagnostic Box refers to one or a combination of machines designed    to perform one or a plurality of aspects of the methods disclosed    herein. In an embodiment, the diagnostic box may be placed at a    point of patient care. In an embodiment, the diagnostic box may    perform targeted amplification followed by sequencing. In an    embodiment the diagnostic box may function alone or with the help of    a technician.-   Informatics Based Method refers to a method that relies heavily on    statistics to make sense of a large amount of data. In the context    of prenatal diagnosis, it refers to a method designed to determine    the ploidy state at one or more chromosomes or the allelic state at    one or more alleles by statistically inferring the most likely    state, rather than by directly physically measuring the state, given    a large amount of genetic data, for example from a molecular array    or sequencing. In an embodiment of the present disclosure, the    informatics based technique may be one disclosed in this patent. In    an embodiment of the present disclosure it may be PARENTAL SUPPORT™.-   Primary Genetic Data refers to the analog intensity signals that are    output by a genotyping platform. In the context of SNP arrays,    primary genetic data refers to the intensity signals before any    genotype calling has been done. In the context of sequencing,    primary genetic data refers to the analog measurements, analogous to    the chromatogram, that comes off the sequencer before the identity    of any base pairs have been determined, and before the sequence has    been mapped to the genome.-   Secondary Genetic Data refers to processed genetic data that are    output by a genotyping platform. In the context of a SNP array, the    secondary genetic data refers to the allele calls made by software    associated with the SNP array reader, wherein the software has made    a call whether a given allele is present or not present in the    sample. In the context of sequencing, the secondary genetic data    refers to the base pair identities of the sequences have been    determined, and possibly also where the sequences have been mapped    to the genome.-   Non-Invasive Prenatal Diagnosis (NPD), or also “Non-Invasive    Prenatal Screening” (NPS), refers to a method of determining the    genetic state of a fetus that is gestating in a mother using genetic    material found in the mother's blood, where the genetic material is    obtained by drawing the mother's intravenous blood.-   Preferential Enrichment of DNA that corresponds to a locus, or    preferential enrichment of DNA at a locus, refers to any method that    results in the percentage of molecules of DNA in a post-enrichment    DNA mixture that correspond to the locus being higher than the    percentage of molecules of DNA in the pre-enrichment DNA mixture    that correspond to the locus. The method may involve selective    amplification of DNA molecules that correspond to a locus. The    method may involve removing DNA molecules that do not correspond to    the locus. The method may involve a combination of methods. The    degree of enrichment is defined as the percentage of molecules of    DNA in the post-enrichment mixture that correspond to the locus    divided by the percentage of molecules of DNA in the pre-enrichment    mixture that correspond to the locus. Preferential enrichment may be    carried out at a plurality of loci. In some embodiments of the    present disclosure, the degree of enrichment is greater than 20. In    some embodiments of the present disclosure, the degree of enrichment    is greater than 200. In some embodiments of the present disclosure,    the degree of enrichment is greater than 2,000. When preferential    enrichment is carried out at a plurality of loci, the degree of    enrichment may refer to the average degree of enrichment of all of    the loci in the set of loci.-   Amplification refers to a method that increases the number of copies    of a molecule of DNA.-   Selective Amplification may refer to a method that increases the    number of copies of a particular molecule of DNA, or molecules of    DNA that correspond to a particular region of DNA. It may also refer    to a method that increases the number of copies of a particular    targeted molecule of DNA, or targeted region of DNA more than it    increases non-targeted molecules or regions of DNA. Selective    amplification may be a method of preferential enrichment.-   Universal Priming Sequence refers to a DNA sequence that may be    appended to a population of target DNA molecules, for example by    ligation, PCR, or ligation mediated PCR. Once added to the    population of target molecules, primers specific to the universal    priming sequences can be used to amplify the target population using    a single pair of amplification primers. Universal priming sequences    are typically not related to the target sequences.-   Universal Adapters, or ‘ligation adaptors’ or ‘library tags’ are DNA    molecules containing a universal priming sequence that can be    covalently linked to the 5-prime and 3-prime end of a population of    target double stranded DNA molecules. The addition of the adapters    provides universal priming sequences to the 5-prime and 3-prime end    of the target population from which PCR amplification can take    place, amplifying all molecules from the target population, using a    single pair of amplification primers.-   Targeting refers to a method used to selectively amplify or    otherwise preferentially enrich those molecules of DNA that    correspond to a set of loci, in a mixture of DNA.-   Joint Distribution Model refers to a model that defines the    probability of events defined in terms of multiple random variables,    given a plurality of random variables defined on the same    probability space, where the probabilities of the variable are    linked. In some embodiments, the degenerate case where the    probabilities of the variables are not linked may be used.

Hypotheses

In the context of this disclosure, a hypothesis refers to a possiblegenetic state. It may refer to a possible ploidy state. It may refer toa possible allelic state. A set of hypotheses may refer to a set ofpossible genetic states, a set of possible allelic states, a set ofpossible ploidy states, or combinations thereof. In some embodiments, aset of hypotheses may be designed such that one hypothesis from the setwill correspond to the actual genetic state of any given individual. Insome embodiments, a set of hypotheses may be designed such that everypossible genetic state may be described by at least one hypothesis fromthe set. In some embodiments of the present disclosure, one aspect of amethod is to determine which hypothesis corresponds to the actualgenetic state of the individual in question.

In another embodiment of the present disclosure, one step involvescreating a hypothesis. In some embodiments it may be a copy numberhypothesis. In some embodiments it may involve a hypothesis concerningwhich segments of a chromosome from each of the related individualscorrespond genetically to which segments, if any, of the other relatedindividuals. Creating a hypothesis may refer to the act of setting thelimits of the variables such that the entire set of possible geneticstates that are under consideration are encompassed by those variables.

A “copy number hypothesis,” also called a “ploidy hypothesis,” or a“ploidy state hypothesis,” may refer to a hypothesis concerning apossible ploidy state for a given chromosome copy, chromosome type, orsection of a chromosome, in the target individual. It may also refer tothe ploidy state at more than one of the chromosome types in theindividual. A set of copy number hypotheses may refer to a set ofhypotheses where each hypothesis corresponds to a different possibleploidy state in an individual. A set of hypotheses may concern a set ofpossible ploidy states, a set of possible parental haplotypescontributions, a set of possible fetal DNA percentages in the mixedsample, or combinations thereof.

A normal individual contains one of each chromosome type from eachparent. However, due to errors in meiosis and mitosis, it is possiblefor an individual to have 0, 1, 2, or more of a given chromosome typefrom each parent. In practice, it is rare to see more that two of agiven chromosomes from a parent. In this disclosure, some embodimentsonly consider the possible hypotheses where 0, 1, or 2 copies of a givenchromosome come from a parent; it is a trivial extension to considermore or less possible copies originating from a parent. In someembodiments, for a given chromosome, there are nine possible hypotheses:the three possible hypothesis concerning 0, 1, or 2 chromosomes ofmaternal origin, multiplied by the three possible hypotheses concerning0, 1, or 2 chromosomes of paternal origin. Let (m,f) refer to thehypothesis where m is the number of a given chromosome inherited fromthe mother, and f is the number of a given chromosome inherited from thefather. Therefore, the nine hypotheses are (0,0), (0,1), (0,2), (1,0),(1,1), (1,2), (2,0), (2,1), and (2,2). These may also be written as H₀₀,H₀₁, H₀₂, H₁₀, H₁₂, H₂₀, H₂₁, and H₂₂. The different hypothesescorrespond to different ploidy states. For example, (1,1) refers to anormal disomic chromosome; (2,1) refers to a maternal trisomy, and (0,1)refers to a paternal monosomy. In some embodiments, the case where twochromosomes are inherited from one parent and one chromosome isinherited from the other parent may be further differentiated into twocases: one where the two chromosomes are identical (matched copy error),and one where the two chromosomes are homologous but not identical(unmatched copy error). In these embodiments, there are sixteen possiblehypotheses. It should be understood that it is possible to use othersets of hypotheses, and a different number of hypotheses.

In some embodiments of the present disclosure, the ploidy hypothesisrefers to a hypothesis concerning which chromosome from other relatedindividuals correspond to a chromosome found in the target individual'sgenome. In some embodiments, a key to the method is the fact thatrelated individuals can be expected to share haplotype blocks, and usingmeasured genetic data from related individuals, along with a knowledgeof which haplotype blocks match between the target individual and therelated individual, it is possible to infer the correct genetic data fora target individual with higher confidence than using the targetindividual's genetic measurements alone. As such, in some embodiments,the ploidy hypothesis may concern not only the number of chromosomes,but also which chromosomes in related individuals are identical, ornearly identical, with one or more chromosomes in the target individual.

Once the set of hypotheses have been defined, when the algorithmsoperate on the input genetic data, they may output a determinedstatistical probability for each of the hypotheses under consideration.The probabilities of the various hypotheses may be determined bymathematically calculating, for each of the various hypotheses, thevalue that the probability equals, as stated by one or more of theexpert techniques, algorithms, and/or methods described elsewhere inthis disclosure, using the relevant genetic data as input.

Once the probabilities of the different hypotheses are estimated, asdetermined by a plurality of techniques, they may be combined. This mayentail, for each hypothesis, multiplying the probabilities as determinedby each technique. The product of the probabilities of the hypothesesmay be normalized. Note that one ploidy hypothesis refers to onepossible ploidy state for a chromosome.

The process of “combining probabilities,” also called “combininghypotheses,” or combining the results of expert techniques, is a conceptthat should be familiar to one skilled in the art of linear algebra. Onepossible way to combine probabilities is as follows: When an experttechnique is used to evaluate a set of hypotheses given a set of geneticdata, the output of the method is a set of probabilities that areassociated, in a one-to-one fashion, with each hypothesis in the set ofhypotheses. When a set of probabilities that were determined by a firstexpert technique, each of which are associated with one of thehypotheses in the set, are combined with a set of probabilities thatwere determined by a second expert technique, each of which areassociated with the same set of hypotheses, then the two sets ofprobabilities are multiplied. This means that, for each hypothesis inthe set, the two probabilities that are associated with that hypothesis,as determined by the two expert methods, are multiplied together, andthe corresponding product is the output probability. This process may beexpanded to any number of expert techniques. If only one experttechnique is used, then the output probabilities are the same as theinput probabilities. If more than two expert techniques are used, thenthe relevant probabilities may be multiplied at the same time. Theproducts may be normalized so that the probabilities of the hypothesesin the set of hypotheses sum to 100%.

In some embodiments, if the combined probabilities for a givenhypothesis are greater than the combined probabilities for any of theother hypotheses, then it may be considered that that hypothesis isdetermined to be the most likely. In some embodiments, a hypothesis maybe determined to be the most likely, and the ploidy state, or othergenetic state, may be called if the normalized probability is greaterthan a threshold. In an embodiment, this may mean that the number andidentity of the chromosomes that are associated with that hypothesis maybe called as the ploidy state. In an embodiment, this may mean that theidentity of the alleles that are associated with that hypothesis may becalled as the allelic state. In some embodiments, the threshold may bebetween about 50% and about 80%. In some embodiments the threshold maybe between about 80% and about 90%. In some embodiments the thresholdmay be between about 90% and about 95%. In some embodiments thethreshold may be between about 95% and about 99%. In some embodimentsthe threshold may be between about 99% and about 99.9%. In someembodiments the threshold may be above about 99.9%.

Parental Contexts

The parental context refers to the genetic state of a given allele, oneach of the two relevant chromosomes for one or both of the two parentsof the target. Note that in an embodiment, the parental context does notrefer to the allelic state of the target, rather, it refers to theallelic state of the parents. The parental context for a given SNP mayconsist of four base pairs, two paternal and two maternal; they may bethe same or different from one another. It is typically written as“m₁m₂|f₁f₂,” where m₁ and m₂ are the genetic state of the given SNP onthe two maternal chromosomes, and f₁ and f₂ are the genetic state of thegiven SNP on the two paternal chromosomes. In some embodiments, theparental context may be written as “f₁f₂|m₁m₂” Note that subscripts “1”and “2” refer to the genotype, at the given allele, of the first andsecond chromosome; also note that the choice of which chromosome islabeled “1” and which is labeled “2” is arbitrary.

Note that in this disclosure, A and B are often used to genericallyrepresent base pair identities; A or B could equally well represent C(cytosine), G (guanine), A (adenine) or T (thymine). For example, if, ata given SNP based allele, the mother's genotype was T at that SNP on onechromosome, and G at that SNP on the homologous chromosome, and thefather's genotype at that allele is G at that SNP on both of thehomologous chromosomes, one may say that the target individual's allelehas the parental context of AB|BB; it could also be said that the allelehas the parental context of ABIAA. Note that, in theory, any of the fourpossible nucleotides could occur at a given allele, and thus it ispossible, for example, for the mother to have a genotype of AT, and thefather to have a genotype of GC at a given allele. However, empiricaldata indicate that in most cases only two of the four possible basepairs are observed at a given allele. It is possible, for example whenusing single tandem repeats, to have more than two parental, more thanfour and even more than ten contexts. In this disclosure the discussionassumes that only two possible base pairs will be observed at a givenallele, although the embodiments disclosed herein could be modified totake into account the cases where this assumption does not hold.

A “parental context” may refer to a set or subset of target SNPs thathave the same parental context. For example, if one were to measure 1000alleles on a given chromosome on a target individual, then the contextAAIBB could refer to the set of all alleles in the group of 1,000alleles where the genotype of the mother of the target was homozygous,and the genotype of the father of the target is homozygous, but wherethe maternal genotype and the paternal genotype are dissimilar at thatlocus. If the parental data is not phased, and thus AB=BA, then thereare nine possible parental contexts: AAIAA, AAIAB, AAIBB, AB|AA, ABIAB,AB|BB, BBIAA, BBIAB, and BBIBB. If the parental data is phased, and thusAB BA, then there are sixteen different possible parental contexts:AAIAA, AAIAB, AAIBA, AAIBB, AB|AA, ABIAB, ABIBA, AB|BB, BAIAA, BAIAB,BAIBA, BAIBB, BBIAA, BBIAB, BBIBA, and BBIBB. Every SNP allele on achromosome, excluding some SNPs on the sex chromosomes, has one of theseparental contexts. The set of SNPs wherein the parental context for oneparent is heterozygous may be referred to as the heterozygous context.

Use of Parental Contexts in NPD

Non-invasive prenatal diagnosis is an important technique that can beused to determine the genetic state of a fetus from genetic materialthat is obtained in a non-invasive manner, for example from a blood drawon the pregnant mother. The blood could be separated and the plasmaisolated, followed by isolation of the plasma DNA. Size selection couldbe used to isolate the DNA of the appropriate length. The DNA may bepreferentially enriched at a set of loci. This DNA can then be measuredby a number of means, such as by hybridizing to a genotyping array andmeasuring the fluorescence, or by sequencing on a high throughputsequencer.

When sequencing is used for ploidy calling of a fetus in the context ofnon-invasive prenatal diagnosis, there are a number of ways to use thesequence data. The most common way one could use the sequence data is tosimply count the number of reads that map to a given chromosome. Forexample, imagine if you are trying to determine the ploidy state ofchromosome 21 on the fetus. Further imagine that the DNA in the sampleis comprised of 10% DNA of fetal origin, and 90% DNA of maternal origin.In this case, you could look at the average number of reads on achromosome which can be expected to be disomic, for example chromosome3, and compare that to the number of read on chromosome 21, where thereads are adjusted for the number of base pairs on that chromosome thatare part of a unique sequence. If the fetus were euploid, one wouldexpect the amount of DNA per unit of genome to be about equal at alllocations (subject to stochastic variations). On the other hand, if thefetus were trisomic at chromosome 21, then one would expect there to bemore slightly more DNA per genetic unit from chromosome 21 than theother locations on the genome. Specifically one would expect there to beabout 5% more DNA from chromosome 21 in the mixture. When sequencing isused to measure the DNA, one would expect about 5% more uniquelymappable reads from chromosome 21 per unique segment than from the otherchromosomes. One could use the observation of an amount of DNA from aparticular chromosome that is higher than a certain threshold, whenadjusted for the number of sequences that are uniquely mappable to thatchromosome, as the basis for an aneuploidy diagnosis. Another methodthat may be used to detect aneuploidy is similar to that above, exceptthat parental contexts could be taken into account.

When considering which alleles to target, one may consider thelikelihood that some parental contexts are likely to be more informativethan others. For example, AAIBB and the symmetric context BB|AA are themost informative contexts, because the fetus is known to carry an allelethat is different from the mother. For reasons of symmetry, both AAIBBand BB|AA contexts may be referred to as AAIBB. Another set ofinformative parental contexts are AAIAB and BBIAB, because in thesecases the fetus has a 50% chance of carrying an allele that the motherdoes not have. For reasons of symmetry, both AAIAB and BBIAB contextsmay be referred to as AAIAB. A third set of informative parentalcontexts are AB|AA and AB|BB, because in these cases the fetus iscarrying a known paternal allele, and that allele is also present in thematernal genome. For reasons of symmetry, both AB|AA and AB|BB contextsmay be referred to as AB|AA. A fourth parental context is ABIAB wherethe fetus has an unknown allelic state, and whatever the allelic state,it is one in which the mother has the same alleles. The fifth parentalcontext is AA|AA, where the mother and father are heterozygous.

Different Implementations of the Presently Disclosed Embodiments

Method are disclosed herein for determining the ploidy state of a targetindividual. The target individual may be a blastomere, an embryo, or afetus. In some embodiments of the present disclosure, a method fordetermining the ploidy state of one or more chromosome in a targetindividual may include any of the steps described in this document, andcombinations thereof:

In some embodiments the source of the genetic material to be used indetermining the genetic state of the fetus may be fetal cells, such asnucleated fetal red blood cells, isolated from the maternal blood. Themethod may involve obtaining a blood sample from the pregnant mother.The method may involve isolating a fetal red blood cell using visualtechniques, based on the idea that a certain combination of colors areuniquely associated with nucleated red blood cell, and a similarcombination of colors is not associated with any other present cell inthe maternal blood. The combination of colors associated with thenucleated red blood cells may include the red color of the hemoglobinaround the nucleus, which color may be made more distinct by staining,and the color of the nuclear material which can be stained, for example,blue. By isolating the cells from maternal blood and spreading them overa slide, and then identifying those points at which one sees both red(from the Hemoglobin) and blue (from the nuclear material) one may beable to identify the location of nucleated red blood cells. One may thenextract those nucleated red blood cells using a micromanipulator, usegenotyping and/or sequencing techniques to measure aspects of thegenotype of the genetic material in those cells.

In an embodiment, one may stain the nucleated red blood cell with a diethat only fluoresces in the presence of fetal hemoglobin and notmaternal hemoglobin, and so remove the ambiguity between whether anucleated red blood cell is derived from the mother or the fetus. Someembodiments of the present disclosure may involve staining or otherwisemarking nuclear material. Some embodiments of the present disclosure mayinvolve specifically marking fetal nuclear material using fetal cellspecific antibodies.

There are many other ways to isolate fetal cells from maternal blood, orfetal DNA from maternal blood, or to enrich samples of fetal geneticmaterial in the presence of maternal genetic material. Some of thesemethods are listed here, but this is not intended to be an exhaustivelist. Some appropriate techniques are listed here for convenience: usingfluorescently or otherwise tagged antibodies, size exclusionchromatography, magnetically or otherwise labeled affinity tags,epigenetic differences, such as differential methylation between thematernal and fetal cells at specific alleles, density gradientcentrifugation succeeded by CD45/14 depletion and CD71-positiveselection from CD45/14 negative-cells, single or double Percollgradients with different osmolalities, or galactose specific lectinmethod.

In an embodiment of the present disclosure, the target individual is afetus, and the different genotype measurements are made on a pluralityof DNA samples from the fetus. In some embodiments of the presentdisclosure, the fetal DNA samples are from isolated fetal cells wherethe fetal cells may be mixed with maternal cells. In some embodiments ofthe present disclosure, the fetal DNA samples are from free floatingfetal DNA, where the fetal DNA may be mixed with free floating maternalDNA. In some embodiments, the fetal dNA samples may be derived frommaternal plasma or maternal blood that contains a mixture of maternalDNA and fetal DNA. In some embodiments, the fetal DNA may be mixed withmaternal DNA in maternal:fetal ratios ranging from 99.9:0.1% to 99:1%;99:1% to 90:10%; 90:10% to 80:20%; 80:20% to 70:30%; 70:30% to 50:50%;50:50% to 10:90%; or 10:90% to 1:99%; 1:99% to 0.1:99.9%.

In some embodiments, the genetic sample may be prepared and/or purified.There are a number of standard procedures known in the art to accomplishsuch an end. In some embodiments, the sample may be centrifuged toseparate various layers. In some embodiments, the DNA may be isolatedusing filtration. In some embodiments, the preparation of the DNA mayinvolve amplification, separation, purification by chromatography,liquid liquid separation, isolation, preferential enrichment,preferential amplification, targeted amplification, or any of a numberof other techniques either known in the art or described herein.

In some embodiments, a method of the present disclosure may involveamplifying DNA. Amplification of the DNA, a process which transforms asmall amount of genetic material to a larger amount of genetic materialthat comprises a similar set of genetic data, can be done by a widevariety of methods, including, but not limited to polymerase chainreaction (PCR). One method of amplifying DNA is whole genomeamplification (WGA). There are a number of methods available for WGA:ligation-mediated PCR (LM-PCR), degenerate oligonucleotide primer PCR(DOP-PCR), and multiple displacement amplification (MDA). In LM-PCR,short DNA sequences called adapters are ligated to blunt ends of DNA.These adapters contain universal amplification sequences, which are usedto amplify the DNA by PCR. In DOP-PCR, random primers that also containuniversal amplification sequences are used in a first round of annealingand PCR. Then, a second round of PCR is used to amplify the sequencesfurther with the universal primer sequences. MDA uses the phi-29polymerase, which is a highly processive and non-specific enzyme thatreplicates DNA and has been used for single-cell analysis. The majorlimitations to amplification of material from a single cell are (1)necessity of using extremely dilute DNA concentrations or extremelysmall volume of reaction mixture, and (2) difficulty of reliablydissociating DNA from proteins across the whole genome. Regardless,single-cell whole genome amplification has been used successfully for avariety of applications for a number of years. There are other methodsof amplifying DNA from a sample of DNA. The DNA amplification transformsthe initial sample of DNA into a sample of DNA that is similar in theset of sequences, but of much greater quantity. In some cases,amplification may not be required.

In some embodiments, DNA may be amplified using a universalamplification, such as WGA or MDA. In some embodiments, DNA may beamplified by targeted amplification, for example using targeted PCR, orcircularizing probes. In some embodiments, the DNA may be preferentiallyenriched using a targeted amplification method, or a method that resultsin the full or partial separation of desired from undesired DNA, such ascapture by hybridization approaches. In some embodiments, DNA may beamplified by using a combination of a universal amplification method anda preferential enrichment method. A fuller description of some of thesemethods can be found elsewhere in this document.

The genetic data of the target individual and/or of the relatedindividual can be transformed from a molecular state to an electronicstate by measuring the appropriate genetic material using tools and ortechniques taken from a group including, but not limited to: genotypingmicroarrays, and high throughput sequencing. Some high throughputsequencing methods include Sanger DNA sequencing, pyrosequencing, theILLUMINA SOLEXA platform, ILLUMINA's GENOME ANALYZER, or APPLIEDBIOSYSTEM's 454 sequencing platform, HELICOS's TRUE SINGLE MOLECULESEQUENCING platform, HALCYON MOLECULAR's electron microscope sequencingmethod, or any other sequencing method. All of these methods physicallytransform the genetic data stored in a sample of DNA into a set ofgenetic data that is typically stored in a memory device en route tobeing processed.

A relevant individual's genetic data may be measured by analyzingsubstances taken from a group including, but not limited to: theindividual's bulk diploid tissue, one or more diploid cells from theindividual, one or more haploid cells from the individual, one or moreblastomeres from the target individual, extra-cellular genetic materialfound on the individual, extra-cellular genetic material from theindividual found in maternal blood, cells from the individual found inmaternal blood, one or more embryos created from (a) gamete(s) from therelated individual, one or more blastomeres taken from such an embryo,extra-cellular genetic material found on the related individual, geneticmaterial known to have originated from the related individual, andcombinations thereof.

In some embodiments, a set of at least one ploidy state hypothesis maybe created for each of the chromosomes types of interest of the targetindividual. Each of the ploidy state hypotheses may refer to onepossible ploidy state of the chromosome or chromosome segment of thetarget individual. The set of hypotheses may include some or all of thepossible ploidy states that the chromosome of the target individual maybe expected to have. Some of the possible ploidy states may includenullsomy, monosomy, disomy, uniparental disomy, euploidy, trisomy,matching trisomy, unmatching trisomy, maternal trisomy, paternaltrisomy, tetrasomy, balanced (2:2) tetrasomy, unbalanced (3:1)tetrasomy, pentasomy, hexasomy, other aneuploidy, and combinationsthereof. Any of these aneuploidy states may be mixed or partialaneuploidy such as unbalanced translocations, balanced translocations,Robertsonian translocations, recombinations, deletions, insertions,crossovers, and combinations thereof.

In some embodiments, the knowledge of the determined ploidy state may beused to make a clinical decision. This knowledge, typically stored as aphysical arrangement of matter in a memory device, may then betransformed into a report. The report may then be acted upon. Forexample, the clinical decision may be to terminate the pregnancy;alternately, the clinical decision may be to continue the pregnancy. Insome embodiments the clinical decision may involve an interventiondesigned to decrease the severity of the phenotypic presentation of agenetic disorder, or a decision to take relevant steps to prepare for aspecial needs child.

In an embodiment of the present disclosure, any of the methods describedherein may be modified to allow for multiple targets to come from sametarget individual, for example, multiple blood draws from the samepregnant mother. This may improve the accuracy of the model, as multiplegenetic measurements may provide more data with which the targetgenotype may be determined. In an embodiment, one set of target geneticdata served as the primary data which was reported, and the other servedas data to double-check the primary target genetic data. In anembodiment, a plurality of sets of genetic data, each measured fromgenetic material taken from the target individual, are considered inparallel, and thus both sets of target genetic data serve to helpdetermine which sections of parental genetic data, measured with highaccuracy, composes the fetal genome.

In an embodiment, the method may be used for the purpose of paternitytesting. For example, given the SNP-based genotypic information from themother, and from a man who may or may not be the genetic father, and themeasured genotypic information from the mixed sample, it is possible todetermine if the genotypic information of the male indeed representsthat actual genetic father of the gestating fetus. A simple way to dothis is to simply look at the contexts where the mother is AA, and thepossible father is AB or BB. In these cases, one may expect to see thefather contribution half (AAIAB) or all (AAIBB) of the time,respectively. Taking into account the expected ADO, it isstraightforward to determine whether or not the fetal SNPs that areobserved are correlated with those of the possible father.

One embodiment of the present disclosure could be as follows: a pregnantwoman wants to know if her fetus is afflicted with Down Syndrome, and/orif it will suffer from Cystic Fibrosis, and she does not wish to bear achild that is afflicted with either of these conditions. A doctor takesher blood, and stains the hemoglobin with one marker so that it appearsclearly red, and stains nuclear material with another marker so that itappears clearly blue. Knowing that maternal red blood cells aretypically anuclear, while a high proportion of fetal cells contain anucleus, the doctor is able to visually isolate a number of nucleatedred blood cells by identifying those cells that show both a red and bluecolor. The doctor picks up these cells off the slide with amicromanipulator and sends them to a lab which amplifies and genotypesten individual cells. By using the genetic measurements, the PARENTALSUPPORT™ method is able to determine that six of the ten cells arematernal blood cells, and four of the ten cells are fetal cells. If achild has already been born to a pregnant mother, PARENTAL SUPPORT™ canalso be used to determine that the fetal cells are distinct from thecells of the born child by making reliable allele calls on the fetalcells and showing that they are dissimilar to those of the born child.Note that this method is similar in concept to the paternal testingembodiment of the present disclosure. The genetic data measured from thefetal cells may be of very poor quality, comprising many allele dropouts, due to the difficulty of genotyping single cells. The clinician isable to use the measured fetal DNA along with the reliable DNAmeasurements of the parents to infer aspects of the genome of the fetuswith high accuracy using PARENTAL SUPPORT™, thereby transforming thegenetic data contained on genetic material from the fetus into thepredicted genetic state of the fetus, stored on a computer. Theclinician is able to determine both the ploidy state of the fetus, andthe presence or absence of a plurality of disease-linked genes ofinterest. It turns out that the fetus is euploid, and is not a carrierfor cystic fibrosis, and the mother decides to continue the pregnancy.

In an embodiment of the present disclosure, a pregnant mother would liketo determine if her fetus is afflicted with any whole chromosomalabnormalities. She goes to her doctor, and gives a sample of her blood,and she and her husband gives samples of their own DNA from cheek swabs.A laboratory researcher genotypes the parental DNA using the MDAprotocol to amplify the parental DNA, and ILLUMINA INFINIUM arrays tomeasure the genetic data of the parents at a large number of SNPs. Theresearcher then spins down the blood, takes the plasma, and isolates asample of free-floating DNA using size exclusion chromatography.Alternately, the researcher uses one or more fluorescent antibodies,such as one that is specific to fetal hemoglobin to isolate a nucleatedfetal red blood cell. The researcher then takes the isolated or enrichedfetal genetic material and amplifies it using a library of 70-meroligonucleotides appropriately designed such that two ends of eacholigonucleotide corresponded to the flanking sequences on either side ofa target allele. Upon addition of a polymerase, ligase, and theappropriate reagents, the oligonucleotides underwent gap-fillingcircularization, capturing the desired allele. An exonuclease was added,heat-inactivated, and the products were used directly as a template forPCR amplification. The PCR products were sequenced on an ILLUMINA GENOMEANALYZER. The sequence reads were used as input for the PARENTALSUPPORT™ method, which then predicted the ploidy state of the fetus.

In another embodiment, a couple—where the mother, who is pregnant, andis of advanced maternal age—wants to know whether the gestating fetushas Down syndrome, Turner Syndrome, Prader Willi syndrome, or some otherwhole chromosomal abnormality. The obstetrician takes a blood draw fromthe mother and father. The blood is sent to a laboratory, where atechnician centrifuges the maternal sample to isolate the plasma and thebuffy coat. The DNA in the buffy coat and the paternal blood sample aretransformed through amplification and the genetic data encoded in theamplified genetic material is further transformed from molecularlystored genetic data into electronically stored genetic data by runningthe genetic material on a high throughput sequencer to measure theparental genotypes. The plasma sample is preferentially enriched at aset of loci using a 5,000-plex hemi-nested targeted PCR method. Themixture of DNA fragments is prepared into a DNA library suitable forsequencing. The DNA is then sequenced using a high throughput sequencingmethod, for example, the ILLUMINA GAIIx GENOME ANALYZER. The sequencingtransforms the information that is encoded molecularly in the DNA intoinformation that is encoded electronically in computer hardware. Aninformatics based technique that includes the presently disclosedembodiments, such as PARENTAL SUPPORT™, may be used to determine theploidy state of the fetus. This may involve calculating, on a computer,allele count probabilities at the plurality of polymorphic loci from theDNA measurements made on the prepared sample; creating, on a computer, aplurality of ploidy hypotheses each pertaining to a different possibleploidy state of the chromosome; building, on a computer, a jointdistribution model for the expected allele counts at the plurality ofpolymorphic loci on the chromosome for each ploidy hypothesis;determining, on a computer, a relative probability of each of the ploidyhypotheses using the joint distribution model and the allele countsmeasured on the prepared sample; and calling the ploidy state of thefetus by selecting the ploidy state corresponding to the hypothesis withthe greatest probability. It is determined that the fetus has Downsyndrome. A report is printed out, or sent electronically to thepregnant woman's obstetrician, who transmits the diagnosis to the woman.The woman, her husband, and the doctor sit down and discuss theiroptions. The couple decides to terminate the pregnancy based on theknowledge that the fetus is afflicted with a trisomic condition.

In an embodiment, a company may decide to offer a diagnostic technologydesigned to detect aneuploidy in a gestating fetus from a maternal blooddraw. Their product may involve a mother presenting to her obstetrician,who may draw her blood. The obstetrician may also collect a geneticsample from the father of the fetus. A clinician may isolate the plasmafrom the maternal blood, and purify the DNA from the plasma. A clinicianmay also isolate the buffy coat layer from the maternal blood, andprepare the DNA from the buffy coat. A clinician may also prepare theDNA from the paternal genetic sample. The clinician may use molecularbiology techniques described in this disclosure to append universalamplification tags to the DNA in the DNA derived from the plasma sample.The clinician may amplify the universally tagged DNA. The clinician maypreferentially enrich the DNA by a number of techniques includingcapture by hybridization and targeted PCR. The targeted PCR may involvenesting, hemi-nesting or semi-nesting, or any other approach to resultin efficient enrichment of the plasma derived DNA. The targeted PCR maybe massively multiplexed, for example with 10,000 primers in onereaction, where the primers target SNPs on chromosomes 13, 18, 21, X andthose loci that are common to both X and Y, and optionally otherchromosomes as well. The selective enrichment and/or amplification mayinvolve tagging each individual molecule with different tags, molecularbarcodes, tags for amplification, and/or tags for sequencing. Theclinician may then sequence the plasma sample, and also possibly alsothe prepared maternal and/or paternal DNA. The molecular biology stepsmay be executed either wholly or partly by a diagnostic box. Thesequence data may be fed into a single computer, or to another type ofcomputing platform such as may be found in ‘the cloud’. The computingplatform may calculate allele counts at the targeted polymorphic locifrom the measurements made by the sequencer. The computing platform maycreate a plurality of ploidy hypotheses pertaining to nullsomy,monosomy, disomy, matched trisomy, and unmatched trisomy for each ofchromosomes 13, 18, 21, X and Y. The computing platform may build ajoint distribution model for the expected allele counts at the targetedloci on the chromosome for each ploidy hypothesis for each of the fivechromosomes being interrogated. The computing platform may determine aprobability that each of the ploidy hypotheses is true using the jointdistribution model and the allele counts measured on the preferentiallyenriched DNA derived from the plasma sample. The computing platform maycall the ploidy state of the fetus, for each of chromosome 13, 18, 21, Xand Y by selecting the ploidy state corresponding to the germanehypothesis with the greatest probability. A report may be generatedcomprising the called ploidy states, and it may be sent to theobstetrician electronically, displayed on an output device, or a printedhard copy of the report may be delivered to the obstetrician. Theobstetrician may inform the patient and optionally the father of thefetus, and they may decide which clinical options are open to them, andwhich is most desirable.

In another embodiment, a pregnant woman, hereafter referred to as “themother” may decide that she wants to know whether or not her fetus(es)are carrying any genetic abnormalities or other conditions. She may wantto ensure that there are not any gross abnormalities before she isconfident to continue the pregnancy. She may go to her obstetrician, whomay take a sample of her blood. He may also take a genetic sample, suchas a buccal swab, from her cheek. He may also take a genetic sample fromthe father of the fetus, such as a buccal swab, a sperm sample, or ablood sample. He may send the samples to a clinician. The clinician mayenrich the fraction of free floating fetal DNA in the maternal bloodsample. The clinician may enrich the fraction of enucleated fetal bloodcells in the maternal blood sample. The clinician may use variousaspects of the methods described herein to determine genetic data of thefetus. That genetic data may include the ploidy state of the fetus,and/or the identity of one or a number of disease linked alleles in thefetus. A report may be generated summarizing the results of the prenataldiagnosis. The report may be transmitted or mailed to the doctor, whomay tell the mother the genetic state of the fetus. The mother maydecide to discontinue the pregnancy based on the fact that the fetus hasone or more chromosomal, or genetic abnormalities, or undesirableconditions. She may also decide to continue the pregnancy based on thefact that the fetus does not have any gross chromosomal or geneticabnormalities, or any genetic conditions of interest.

Another example may involve a pregnant woman who has been artificiallyinseminated by a sperm donor, and is pregnant. She wants to minimize therisk that the fetus she is carrying has a genetic disease. She has blooddrawn at a phlebotomist, and techniques described in this disclosure areused to isolate three nucleated fetal red blood cells, and a tissuesample is also collected from the mother and genetic father. The geneticmaterial from the fetus and from the mother and father are amplified asappropriate and genotyped using the ILLUMINA INFINIUM BEADARRAY, and themethods described herein clean and phase the parental and fetal genotypewith high accuracy, as well as to make ploidy calls for the fetus. Thefetus is found to be euploid, and phenotypic susceptibilities arepredicted from the reconstructed fetal genotype, and a report isgenerated and sent to the mother's physician so that they can decidewhat clinical decisions may be best.

In an embodiment, the raw genetic material of the mother and the fatheris transformed by way of amplification to an amount of DNA that issimilar in sequence, but larger in quantity. Then, by way of agenotyping method, the genotypic data that is encoded by nucleic acidsis transformed into genetic measurements that may be stored physicallyand/or electronically on a memory device, such as those described above.The relevant algorithms that makeup the PARENTAL SUPPORT™ algorithm,relevant parts of which are discussed in detail herein, are translatedinto a computer program, using a programming language. Then, through theexecution of the computer program on the computer hardware, instead ofbeing physically encoded bits and bytes, arranged in a pattern thatrepresents raw measurement data, they become transformed into a patternthat represents a high confidence determination of the ploidy state ofthe fetus. The details of this transformation will rely on the dataitself and the computer language and hardware system used to execute themethod described herein. Then, the data that is physically configured torepresent a high quality ploidy determination of the fetus istransformed into a report which may be sent to a health carepractitioner. This transformation may be carried out using a printer ora computer display. The report may be a printed copy, on paper or othersuitable medium, or else it may be electronic. In the case of anelectronic report, it may be transmitted, it may be physically stored ona memory device at a location on the computer accessible by the healthcare practitioner; it also may be displayed on a screen so that it maybe read. In the case of a screen display, the data may be transformed toa readable format by causing the physical transformation of pixels onthe display device. The transformation may be accomplished by way ofphysically firing electrons at a phosphorescent screen, by way ofaltering an electric charge that physically changes the transparency ofa specific set of pixels on a screen that may lie in front of asubstrate that emits or absorbs photons. This transformation may beaccomplished by way of changing the nanoscale orientation of themolecules in a liquid crystal, for example, from nematic to cholestericor sematic phase, at a specific set of pixels. This transformation maybe accomplished by way of an electric current causing photons to beemitted from a specific set of pixels made from a plurality of lightemitting diodes arranged in a meaningful pattern. This transformationmay be accomplished by any other way used to display information, suchas a computer screen, or some other output device or way of transmittinginformation. The health care practitioner may then act on the report,such that the data in the report is transformed into an action. Theaction may be to continue or discontinue the pregnancy, in which case agestating fetus with a genetic abnormality is transformed intonon-living fetus. The transformations listed herein may be aggregated,such that, for example, one may transform the genetic material of apregnant mother and the father, through a number of steps outlined inthis disclosure, into a medical decision consisting of aborting a fetuswith genetic abnormalities, or consisting of continuing the pregnancy.Alternately, one may transform a set of genotypic measurements into areport that helps a physician treat his pregnant patient.

In an embodiment of the present disclosure, the method described hereincan be used to determine the ploidy state of a fetus even when the hostmother, i.e. the woman who is pregnant, is not the biological mother ofthe fetus she is carrying. In an embodiment of the present disclosure,the method described herein can be used to determine the ploidy state ofa fetus using only the maternal blood sample, and without the need for apaternal genetic sample.

Some of the math in the presently disclosed embodiments makes hypothesesconcerning a limited number of states of aneuploidy. In some cases, forexample, only zero, one or two chromosomes are expected to originatefrom each parent. In some embodiments of the present disclosure, themathematical derivations can be expanded to take into account otherforms of aneuploidy, such as quadrosomy, where three chromosomesoriginate from one parent, pentasomy, hexasomy etc., without changingthe fundamental concepts of the present disclosure. At the same time, itis possible to focus on a smaller number of ploidy states, for example,only trisomy and disomy. Note that ploidy determinations that indicate anon-whole number of chromosomes may indicate mosaicism in a sample ofgenetic material.

In some embodiments, the genetic abnormality is a type of aneuploidy,such as Down syndrome (or trisomy 21), Edwards syndrome (trisomy 18),Patau syndrome (trisomy 13), Turner Syndrome (45X), Klinefelter'ssyndrome (a male with 2 X chromosomes), Prader-Willi syndrome, andDiGeorge syndrome (UPD 15). Congenital disorders, such as those listedin the prior sentence, are commonly undesirable, and the knowledge thata fetus is afflicted with one or more phenotypic abnormalities mayprovide the basis for a decision to terminate the pregnancy, to takenecessary precautions to prepare for the birth of a special needs child,or to take some therapeutic approach meant to lessen the severity of achromosomal abnormality.

In some embodiments, the methods described herein can be used at a veryearly gestational age, for example as early as four weeks, as early asfive weeks, as early as six weeks, as early as seven weeks, as early aseight weeks, as early as nine weeks, as early as ten weeks, as early aseleven weeks, and as early as twelve weeks.

Note that it has been demonstrated that DNA that originated from cancerthat is living in a host can be found in the blood of the host. In thesame way that genetic diagnoses can be made from the measurement ofmixed DNA found in maternal blood, genetic diagnoses can equally well bemade from the measurement of mixed DNA found in host blood. The geneticdiagnoses may include aneuploidy states, or gene mutations. Any claim inthe instant disclosure that reads on determining the ploidy state orgenetic state of a fetus from the measurements made on maternal bloodcan equally well read on determining the ploidy state or genetic stateof a cancer from the measurements on host blood.

In some embodiments, a method of the present disclosure allows one todetermine the ploidy status of a cancer, the method including obtaininga mixed sample that contains genetic material from the host, and geneticmaterial from the cancer; measuring the DNA in the mixed sample;calculating the fraction of DNA that is of cancer origin in the mixedsample; and determining the ploidy status of the cancer using themeasurements made on the mixed sample and the calculated fraction. Insome embodiments, the method may further include administering a cancertherapeutic based on the determination of the ploidy state of thecancer. In some embodiments, the method may further includeadministering a cancer therapeutic based on the determination of theploidy state of the cancer, wherein the cancer therapeutic is taken fromthe group comprising a pharmaceutical, a biologic therapeutic, andantibody based therapy and combination thereof.

In some embodiments, a method disclosed herein is used in the context ofpre-implantation genetic diagnosis (PGD) for embryo selection during invitro fertilization, where the target individual is an embryo, and theparental genotypic data can be used to make ploidy determinations aboutthe embryo from sequencing data from a single or two cell biopsy from aday 3 embryo or a trophectoderm biopsy from a day 5 or day 6 embryo. Ina PGD setting, only the child DNA is measured, and only a small numberof cells are tested, generally one to five but as many as ten, twenty orfifty. The total number of starting copies of the A and B alleles (at aSNP) are then trivially determined by the child genotype and the numberof cells. In NPD, the number of starting copies is very high and so theallele ratio after PCR is expected to accurately reflect the startingratio. However, the small number of starting copies in PGD means thatcontamination and imperfect PCR efficiency have a non-trivial effect onthe allele ratio following PCR. This effect may be more important thandepth of read in predicting the variance in the allele ratio measuredafter sequencing. The distribution of measured allele ratio given aknown child genotype may be created by Monte Carlo simulation of the PCRprocess based on the PCR probe efficiency and probability ofcontamination. Given an allele ratio distribution for each possiblechild genotype, the likelihoods of various hypotheses can be calculatedas described for NIPD.

Any of the embodiments disclosed herein may be implemented in digitalelectronic circuitry, integrated circuitry, specially designed ASICs(application-specific integrated circuits), computer hardware, firmware,software, or in combinations thereof. Apparatus of the presentlydisclosed embodiments can be implemented in a computer program producttangibly embodied in a machine-readable storage device for execution bya programmable processor; and method steps of the presently disclosedembodiments can be performed by a programmable processor executing aprogram of instructions to perform functions of the presently disclosedembodiments by operating on input data and generating output. Thepresently disclosed embodiments can be implemented advantageously in oneor more computer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device. Eachcomputer program can be implemented in a high-level procedural orobject-oriented programming language or in assembly or machine languageif desired; and in any case, the language can be a compiled orinterpreted language. A computer program may be deployed in any form,including as a stand-alone program, or as a module, component,subroutine, or other unit suitable for use in a computing environment. Acomputer program may be deployed to be executed or interpreted on onecomputer or on multiple computers at one site, or distributed acrossmultiple sites and interconnected by a communication network.

Computer readable storage media, as used herein, refers to physical ortangible storage (as opposed to signals) and includes without limitationvolatile and non-volatile, removable and non-removable media implementedin any method or technology for the tangible storage of information suchas computer-readable instructions, data structures, program modules orother data. Computer readable storage media includes, but is not limitedto, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other physical or material medium which can be used to tangiblystore the desired information or data or instructions and which can beaccessed by a computer or processor.

Any of the methods described herein may include the output of data in aphysical format, such as on a computer screen, or on a paper printout.In explanations of any embodiments elsewhere in this document, it shouldbe understood that the described methods may be combined with the outputof the actionable data in a format that can be acted upon by aphysician. In addition, the described methods may be combined with theactual execution of a clinical decision that results in a clinicaltreatment, or the execution of a clinical decision to make no action.Some of the embodiments described in the document for determininggenetic data pertaining to a target individual may be combined with thedecision to select one or more embryos for transfer in the context ofIVF, optionally combined with the process of transferring the embryo tothe womb of the prospective mother. Some of the embodiments described inthe document for determining genetic data pertaining to a targetindividual may be combined with the notification of a potentialchromosomal abnormality, or lack thereof, with a medical professional,optionally combined with the decision to abort, or to not abort, a fetusin the context of prenatal diagnosis. Some of the embodiments describedherein may be combined with the output of the actionable data, and theexecution of a clinical decision that results in a clinical treatment,or the execution of a clinical decision to make no action.

Targeted Enrichment and Sequencing

The use of a technique to enrich a sample of DNA at a set of target locifollowed by sequencing as part of a method for non-invasive prenatalallele calling or ploidy calling may confer a number of unexpectedadvantages. In some embodiments of the present disclosure, the methodinvolves measuring genetic data for use with an informatics basedmethod, such as PARENTAL SUPPORT™ (PS). The ultimate outcome of some ofthe embodiments is the actionable genetic data of an embryo or a fetus.There are many methods that may be used to measure the genetic data ofthe individual and/or the related individuals as part of embodiedmethods. In an embodiment, a method for enriching the concentration of aset of targeted alleles is disclosed herein, the method comprising oneor more of the following steps: targeted amplification of geneticmaterial, addition of loci specific oligonucleotide probes, ligation ofspecified DNA strands, isolation of sets of desired DNA, removal ofunwanted components of a reaction, detection of certain sequences of DNAby hybridization, and detection of the sequence of one or a plurality ofstrands of DNA by DNA sequencing methods. In some cases the DNA strandsmay refer to target genetic material, in some cases they may refer toprimers, in some cases they may refer to synthesized sequences, orcombinations thereof. These steps may be carried out in a number ofdifferent orders. Given the highly variable nature of molecular biology,it is generally not obvious which methods, and which combinations ofsteps, will perform poorly, well, or best in various situations.

For example, a universal amplification step of the DNA prior to targetedamplification may confer several advantages, such as removing the riskof bottlenecking and reducing allelic bias. The DNA may be mixed anoligonucleotide probe that can hybridize with two neighboring regions ofthe target sequence, one on either side. After hybridization, the endsof the probe may be connected by adding a polymerase, a means forligation, and any necessary reagents to allow the circularization of theprobe. After circularization, an exonuclease may be added to digest tonon-circularized genetic material, followed by detection of thecircularized probe. The DNA may be mixed with PCR primers that canhybridize with two neighboring regions of the target sequence, one oneither side. After hybridization, the ends of the probe may be connectedby adding a polymerase, a means for ligation, and any necessary reagentsto complete PCR amplification. Amplified or unamplified DNA may betargeted by hybrid capture probes that target a set of loci; afterhybridization, the probe may be localized and separated from the mixtureto provide a mixture of DNA that is enriched in target sequences.

In some embodiments the detection of the target genetic material may bedone in a multiplexed fashion. The number of genetic target sequencesthat may be run in parallel can range from one to ten, ten to onehundred, one hundred to one thousand, one thousand to ten thousand, tenthousand to one hundred thousand, one hundred thousand to one million,or one million to ten million. Note that the prior art includesdisclosures of successful multiplexed PCR reactions involving pools ofup to about 50 or 100 primers, and not more. Prior attempts to multiplexmore than 100 primers per pool have resulted in significant problemswith unwanted side reactions such as primer-dimer formation.

In some embodiments, this method may be used to genotype a single cell,a small number of cells, two to five cells, six to ten cells, ten totwenty cells, twenty to fifty cell, fifty to one hundred cells, onehundred to one thousand cells, or a small amount of extracellular DNA,for example from one to ten picograms, from ten to one hundredpictograms, from one hundred pictograms to one nanogram, from one to tennanograms, from ten to one hundred nanograms, or from one hundrednanograms to one microgram.

The use of a method to target certain loci followed by sequencing aspart of a method for allele calling or ploidy calling may confer anumber of unexpected advantages. Some methods by which DNA may betargeted, or preferentially enriched, include using circularizingprobes, linked inverted probes (LIPs, MIPs), capture by hybridizationmethods such as SURESELECT, and targeted PCR or ligation-mediated PCRamplification strategies.

In some embodiments, a method of the present disclosure involvesmeasuring genetic data for use with an informatics based method, such asPARENTAL SUPPORT™ (PS). PARENTAL SUPPORT™ is an informatics basedapproach to manipulating genetic data, aspects of which are describedherein. The ultimate outcome of some of the embodiments is theactionable genetic data of an embryo or a fetus followed by a clinicaldecision based on the actionable data. The algorithms behind the PSmethod take the measured genetic data of the target individual, often anembryo or fetus, and the measured genetic data from related individuals,and are able to increase the accuracy with which the genetic state ofthe target individual is known. In an embodiment, the measured geneticdata is used in the context of making ploidy determinations duringprenatal genetic diagnosis. In an embodiment, the measured genetic datais used in the context of making ploidy determinations or allele callson embryos during in vitro fertilization. There are many methods thatmay be used to measure the genetic data of the individual and/or therelated individuals in the aforementioned contexts. The differentmethods comprise a number of steps, those steps often involvingamplification of genetic material, addition of olgionucleotide probes,ligation of specified DNA strands, isolation of sets of desired DNA,removal of unwanted components of a reaction, detection of certainsequences of DNA by hybridization, detection of the sequence of one or aplurality of strands of DNA by DNA sequencing methods. In some cases,the DNA strands may refer to target genetic material, in some cases theymay refer to primers, in some cases they may refer to synthesizedsequences, or combinations thereof. These steps may be carried out in anumber of different orders. Given the highly variable nature ofmolecular biology, it is generally not obvious which methods, and whichcombinations of steps, will perform poorly, well, or best in varioussituations.

Note that in theory it is possible to target any number loci in thegenome, anywhere from one loci to well over one million loci. If asample of DNA is subjected to targeting, and then sequenced, thepercentage of the alleles that are read by the sequencer will beenriched with respect to their natural abundance in the sample. Thedegree of enrichment can be anywhere from one percent (or even less) toten-fold, a hundred-fold, a thousand-fold or even many million-fold. Inthe human genome there are roughly 3 billion base pairs, andnucleotides, comprising approximately 75 million polymorphic loci. Themore loci that are targeted, the smaller the degree of enrichment ispossible. The fewer the number of loci that are targeted, the greaterdegree of enrichment is possible, and the greater depth of read may beachieved at those loci for a given number of sequence reads.

In an embodiment of the present disclosure, the targeting orpreferential may focus entirely on SNPs. In an embodiment, the targetingor preferential may focus on any polymorphic site. A number ofcommercial targeting products are available to enrich exons.Surprisingly, targeting exclusively SNPs, or exclusively polymorphicloci, is particularly advantageous when using a method for NPD thatrelies on allele distributions. There are also published methods for NPDusing sequencing, for example U.S. Pat. No. 7,888,017, involving a readcount analysis where the read counting focuses on counting the number ofreads that map to a given chromosome, where the analyzed sequence readsdo not focused on regions of the genome that are polymorphic. Thosetypes of methodology that do not focus on polymorphic alleles would notbenefit as much from targeting or preferential enrichment of a set ofalleles.

In an embodiment of the present disclosure, it is possible to use atargeting method that focuses on SNPs to enrich a genetic sample inpolymorphic regions of the genome. In an embodiment, it is possible tofocus on a small number of SNPs, for example between 1 and 100 SNPs, ora larger number, for example, between 100 and 1,000, between 1,000 and10,000, between 10,000 and 100,000 or more than 100,000 SNPs. In anembodiment, it is possible to focus on one or a small number ofchromosomes that are correlated with live trisomic births, for examplechromosomes 13, 18, 21, X and Y, or some combination thereof. In anembodiment, it is possible to enrich the targeted SNPs by a smallfactor, for example between 1.01 fold and 100 fold, or by a largerfactor, for example between 100 fold and 1,000,000 fold, or even by morethan 1,000,000 fold. In an embodiment of the present disclosure, it ispossible to use a targeting method to create a sample of DNA that ispreferentially enriched in polymorphic regions of the genome. In anembodiment, it is possible to use this method to create a mixture of DNAwith any of these characteristics where the mixture of DNA containsmaternal DNA and also free floating fetal DNA. In an embodiment, it ispossible to use this method to create a mixture of DNA that has anycombination of these factors. For example, the method described hereinmay be used to produce a mixture of DNA that comprises maternal DNA andfetal DNA, and that is preferentially enriched in DNA that correspondsto 200 SNPs, all of which are located on either chromosome 18 or 21, andwhich are enriched an average of 1000 fold. In another example, it ispossible to use the method to create a mixture of DNA that ispreferentially enriched in 10,000 SNPs that are all or mostly located onchromosomes 13, 18, 21, X and Y, and the average enrichment per loci isgreater than 500 fold. Any of the targeting methods described herein canbe used to create mixtures of DNA that are preferentially enriched incertain loci.

In some embodiments, a method of the present disclosure further includesmeasuring the DNA in the mixed fraction using a high throughput DNAsequencer, where the DNA in the mixed fraction contains adisproportionate number of sequences from one or more chromosomes,wherein the one or more chromosomes are taken from the group comprisingchromosome 13, chromosome 18, chromosome 21, chromosome X, chromosome Yand combinations thereof.

Described herein are three methods: multiplex PCR, targeted capture byhybridization, and linked inverted probes (LIPs), which may be used toobtain and analyze measurements from a sufficient number of polymorphicloci from a maternal plasma sample in order to detect fetal aneuploidy;this is not meant to exclude other methods of selective enrichment oftargeted loci. Other methods may equally well be used without changingthe essence of the method. In each case the polymorphism assayed mayinclude single nucleotide polymorphisms (SNPs), small indels, or STRs. Apreferred method involves the use of SNPs. Each approach produces allelefrequency data; allele frequency data for each targeted locus and/or thejoint allele frequency distributions from these loci may be analyzed todetermine the ploidy of the fetus. Each approach has its ownconsiderations due to the limited source material and the fact thatmaternal plasma consists of mixture of maternal and fetal DNA. Thismethod may be combined with other approaches to provide a more accuratedetermination. In an embodiment, this method may be combined with asequence counting approach such as that described in U.S. Pat. No.7,888,017. The approaches described could also be used to detect fetalpaternity noninvasively from maternal plasma samples. In addition, eachapproach may be applied to other mixtures of DNA or pure DNA samples todetect the presence or absence of aneuploid chromosomes, to genotype alarge number of SNP from degraded DNA samples, to detect segmental copynumber variations (CNVs), to detect other genotypic states of interest,or some combination thereof.

Accurately Measuring the Allelic Distributions in a Sample

Current sequencing approaches can be used to estimate the distributionof alleles in a sample. One such method involves randomly samplingsequences from a pool DNA, termed shotgun sequencing. The proportion ofa particular allele in the sequencing data is typically very low and canbe determined by simple statistics. The human genome containsapproximately 3 billion base pairs. So, if the sequencing method usedmake 100 bp reads, a particular allele will be measured about once inevery 30 million sequence reads.

In an embodiment, a method of the present disclosure is used todetermine the presence or absence of two or more different haplotypesthat contain the same set of loci in a sample of DNA from the measuredallele distributions of loci from that chromosome. The differenthaplotypes could represent two different homologous chromosomes from oneindividual, three different homologous chromosomes from a trisomicindividual, three different homologous haplotypes from a mother and afetus where one of the haplotypes is shared between the mother and thefetus, three or four haplotypes from a mother and fetus where one or twoof the haplotypes are shared between the mother and the fetus, or othercombinations. Alleles that are polymorphic between the haplotypes tendto be more informative, however any alleles where the mother and fatherare not both homozygous for the same allele will yield usefulinformation through measured allele distributions beyond the informationthat is available from simple read count analysis.

Shotgun sequencing of such a sample, however, is extremely inefficientas it results in many sequences for regions that are not polymorphicbetween the different haplotypes in the sample, or are for chromosomesthat are not of interest, and therefore reveal no information about theproportion of the target haplotypes. Described herein are methods thatspecifically target and/or preferentially enrich segments of DNA in thesample that are more likely to be polymorphic in the genome to increasethe yield of allelic information obtained by sequencing. Note that forthe measured allele distributions in an enriched sample to be trulyrepresentative of the actual amounts present in the target individual,it is critical that there is little or no preferential enrichment of oneallele as compared to the other allele at a given loci in the targetedsegments. Current methods known in the art to target polymorphic allelesare designed to ensure that at least some of any alleles present aredetected. However, these methods were not designed for the purpose ofmeasuring the unbiased allelic distributions of polymorphic allelespresent in the original mixture. It is non-obvious that any particularmethod of target enrichment would be able to produce an enriched samplewherein the measured allele distributions would accurately represent theallele distributions present in the original unamplified sample betterthan any other method. While many enrichment methods may be expected, intheory, to accomplish such an aim, an ordinary person skilled in the artis well aware that there is a great deal of stochastic or deterministicbias in current amplification, targeting and other preferentialenrichment methods. One embodiment of a method described herein allows aplurality of alleles found in a mixture of DNA that correspond to agiven locus in the genome to be amplified, or preferentially enriched ina way that the degree of enrichment of each of the alleles is nearly thesame. Another way to say this is that the method allows the relativequantity of the alleles present in the mixture as a whole to beincreased, while the ratio between the alleles that correspond to eachlocus remains essentially the same as they were in the original mixtureof DNA. Methods in the prior art preferential enrichment of loci canresult in allelic biases of more than 1%, more than 2%, more than 5% andeven more than 10%. This preferential enrichment may be due to capturebias when using a capture by hybridization approach, or amplificationbias which may be small for each cycle, but can become large whencompounded over 20, 30 or 40 cycles. For the purposes of thisdisclosure, for the ratio to remain essentially the same means that theratio of the alleles in the original mixture divided by the ratio of thealleles in the resulting mixture is between 0.95 and 1.05, between 0.98and 1.02, between 0.99 and 1.01, between 0.995 and 1.005, between 0.998and 1.002, between 0.999 and 1.001, or between 0.9999 and 1.0001. Notethat the calculation of the allele ratios presented here may not be usedin the determination of the ploidy state of the target individual, andmay only a metric to be used to measure allelic bias.

In an embodiment, once a mixture has been preferentially enriched at theset of target loci, it may be sequenced using any one of the previous,current, or next generation of sequencing instruments that sequences aclonal sample (a sample generated from a single molecule; examplesinclude ILLUMINA GAIIx, ILLUMINA HISEQ, LIFE TECHNOLOGIES SOLiD,5500XL). The ratios can be evaluated by sequencing through the specificalleles within the targeted region. These sequencing reads can beanalyzed and counted according the allele type and the rations ofdifferent alleles determined accordingly. For variations that are one toa few bases in length, detection of the alleles will be performed bysequencing and it is essential that the sequencing read span the allelein question in order to evaluate the allelic composition of thatcaptured molecule. The total number of captured molecules assayed forthe genotype can be increased by increasing the length of the sequencingread. Full sequencing of all molecules would guarantee collection of themaximum amount of data available in the enriched pool. However,sequencing is currently expensive, and a method that can measure alleledistributions using a lower number of sequence reads will have greatvalue. In addition, there are technical limitations to the maximumpossible length of read as well as accuracy limitations as read lengthsincrease. The alleles of greatest utility will be of one to a few basesin length, but theoretically any allele shorter than the length of thesequencing read can be used. While allele variations come in all types,the examples provided herein focus on SNPs or variants contained of justa few neighboring base pairs. Larger variants such as segmental copynumber variants can be detected by aggregations of these smallervariations in many cases as whole collections of SNP internal to thesegment are duplicated. Variants larger than a few bases, such as STRsrequire special consideration and some targeting approaches work whileothers will not.

There are multiple targeting approaches that can be used to specificallyisolate and enrich a one or a plurality of variant positions in thegenome. Typically, these rely on taking advantage of the invariantsequence flanking the variant sequence. There is prior art related totargeting in the context of sequencing where the substrate is maternalplasma (see, e.g., Liao et al., Clin. Chem. 2011; 57(1): pp. 92-101).However, the approaches in the prior art all use targeting probes thattarget exons, and do not focus on targeting polymorphic regions of thegenome. In an embodiment, a method of the present disclosure involvesusing targeting probes that focus exclusively or almost exclusively onpolymorphic regions. In an embodiment, a method of the presentdisclosure involves using targeting probes that focus exclusively oralmost exclusively on SNPs. In some embodiments of the presentdisclosure, the targeted polymorphic sites consist of at least 10% SNPs,at least 20% SNPs, at least 30% SNPs, at least 40% SNPs, at least 50%SNPs, at least 60% SNPs, at least 70% SNPs, at least 80% SNPs, at least90% SNPs, at least 95% SNPs, at least 98% SNPs, at least 99% SNPs, atleast 99.9% SNPs, or exclusively SNPs.

In an embodiment, a method of the present disclosure can be used todetermine genotypes (base composition of the DNA at specific loci) andrelative proportions of those genotypes from a mixture of DNA molecules,where those DNA molecules may have originated from one or a number ofgenetically distinct individuals. In an embodiment, a method of thepresent disclosure can be used to determine the genotypes at a set ofpolymorphic loci, and the relative ratios of the amount of differentalleles present at those loci. In an embodiment the polymorphic loci mayconsist entirely of SNPs. In an embodiment, the polymorphic loci cancomprise SNPs, single tandem repeats, and other polymorphisms. In anembodiment, a method of the present disclosure can be used to determinethe relative distributions of alleles at a set of polymorphic loci in amixture of DNA, where the mixture of DNA comprises DNA that originatesfrom a mother, and DNA that originates from a fetus. In an embodiment,the joint allele distributions can be determined on a mixture of DNAisolated from blood from a pregnant woman. In an embodiment, the alleledistributions at a set of loci can be used to determine the ploidy stateof one or more chromosomes on a gestating fetus.

In an embodiment, the mixture of DNA molecules could be derived from DNAextracted from multiple cells of one individual. In an embodiment, theoriginal collection of cells from which the DNA is derived may comprisea mixture of diploid or haploid cells of the same or of differentgenotypes, if that individual is mosaic (germline or somatic). In anembodiment, the mixture of DNA molecules could also be derived from DNAextracted from single cells. In an embodiment, the mixture of DNAmolecules could also be derived from DNA extracted from mixture of twoor more cells of the same individual, or of different individuals. In anembodiment, the mixture of DNA molecules could be derived from DNAisolated from biological material that has already liberated from cellssuch as blood plasma, which is known to contain cell free DNA. In anembodiment, this biological material may be a mixture of DNA from one ormore individuals, as is the case during pregnancy where it has beenshown that fetal DNA is present in the mixture. In an embodiment, thebiological material could be from a mixture of cells that were found inmaternal blood, where some of the cells are fetal in origin. In anembodiment, the biological material could be cells from the blood of apregnant which have been enriched in fetal cells.

Circularizing Probes

Some embodiments of the present disclosure involve the use of “LinkedInverted Probes” (LIPs), which have been previously described in theliterature. LIPs is a generic term meant to encompass technologies thatinvolve the creation of a circular molecule of DNA, where the probes aredesigned to hybridize to targeted region of DNA on either side of atargeted allele, such that addition of appropriate polymerases and/orligases, and the appropriate conditions, buffers and other reagents,will complete the complementary, inverted region of DNA across thetargeted allele to create a circular loop of DNA that captures theinformation found in the targeted allele. LIPs may also be calledpre-circularized probes, pre-circularizing probes, or circularizingprobes. The LIPs probe may be a linear DNA molecule between 50 and 500nucleotides in length, and in an embodiment between 70 and 100nucleotides in length; in some embodiments, it may be longer or shorterthan described herein. Others embodiments of the present disclosureinvolve different incarnations, of the LIPs technology, such as PadlockProbes and MOLECULAR INVERSION PROBES (MIPs).

One method to target specific locations for sequencing is to synthesizeprobes in which the 3′ and 5′ ends of the probes anneal to target DNA atlocations adjacent to and on either side of the targeted region, in aninverted manner, such that the addition of DNA polymerase and DNA ligaseresults in extension from the 3′ end, adding bases to single strandedprobe that are complementary to the target molecule (gap-fill), followedby ligation of the new 3′ end to the 5′ end of the original proberesulting in a circular DNA molecule that can be subsequently isolatedfrom background DNA. The probe ends are designed to flank the targetedregion of interest. One aspect of this approach is commonly called MIPSand has been used in conjunction with array technologies to determinethe nature of the sequence filled in. One drawback to the use of MIPs inthe context of measuring allele ratios is that the hybridization,circularization and amplification steps do not happen at equal rates fordifferent alleles at the same loci. This results in measured alleleratios that are not representative of the actual allele ratios presentin the original mixture.

In an embodiment, the circularizing probes are constructed such that theregion of the probe that is designed to hybridize upstream of thetargeted polymorphic locus and the region of the probe that is designedto hybridize downstream of the targeted polymorphic locus are covalentlyconnected through a non-nucleic acid backbone. This backbone can be anybiocompatible molecule or combination of biocompatible molecules. Someexamples of possible biocompatible molecules are poly(ethylene glycol),polycarbonates, polyurethanes, polyethylenes, polypropylenes, sulfonepolymers, silicone, cellulose, fluoropolymers, acrylic compounds,styrene block copolymers, and other block copolymers.

In an embodiment of the present disclosure, this approach has beenmodified to be easily amenable to sequencing as a means of interrogatingthe filled in sequence. In order to retain the original allelicproportions of the original sample at least one key consideration mustbe taken into account. The variable positions among different alleles inthe gap-fill region must not be too close to the probe binding sites asthere can be initiation bias by the DNA polymerase resulting indifferential of the variants. Another consideration is that additionalvariations may be present in the probe binding sites that are correlatedto the variants in the gap-fill region which can result unequalamplification from different alleles. In an embodiment of the presentdisclosure, the 3′ ends and 5′ ends of the pre-circularized probe aredesigned to hybridize to bases that are one or a few positions away fromthe variant positions (polymorphic sites) of the targeted allele. Thenumber of bases between the polymorphic site (SNP or otherwise) and thebase to which the 3′ end and/or 5′ of the pre-circularized probe isdesigned to hybridize may be one base, it may be two bases, it may bethree bases, it may be four bases, it may be five bases, it may be sixbases, it may be seven to ten bases, it may be eleven to fifteen bases,or it may be sixteen to twenty bases, twenty to thirty bases, or thirtyto sixty bases. The forward and reverse primers may be designed tohybridize a different number of bases away from the polymorphic site.Circularizing probes can be generated in large numbers with current DNAsynthesis technology allowing very large numbers of probes to begenerated and potentially pooled, enabling interrogation of many locisimultaneously. It has been reported to work with more than 300,000probes. Two papers that discuss a method involving circularizing probesthat can be used to measure the genomic data of the target individualinclude: Porreca et al., Nature Methods, 2007 4(11), pp. 931-936; andalso Turner et al., Nature Methods, 2009, 6(5), pp. 315-316. The methodsdescribed in these papers may be used in combination with other methodsdescribed herein. Certain steps of the method from these two papers maybe used in combination with other steps from other methods describedherein.

In some embodiments of the methods disclosed herein, the geneticmaterial of the target individual is optionally amplified, followed byhybridization of the pre-circularized probes, performing a gap fill tofill in the bases between the two ends of the hybridized probes,ligating the two ends to form a circularized probe, and amplifying thecircularized probe, using, for example, rolling circle amplification.Once the desired target allelic genetic information is captured bycircularizing appropriately designed oligonucleic probes, such as in theLIPs system, the genetic sequence of the circularized probes may bebeing measured to give the desired sequence data. In an embodiment, theappropriately designed oligonucleotides probes may be circularizeddirectly on unamplified genetic material of the target individual, andamplified afterwards. Note that a number of amplification procedures maybe used to amplify the original genetic material, or the circularizedLIPs, including rolling circle amplification, MDA, or otheramplification protocols. Different methods may be used to measure thegenetic information on the target genome, for example using highthroughput sequencing, Sanger sequencing, other sequencing methods,capture-by-hybridization, capture-by-circularization, multiplex PCR,other hybridization methods, and combinations thereof.

Once the genetic material of the individual has been measured using oneor a combination of the above methods, an informatics based method, suchas the PARENTAL SUPPORT™ method, along with the appropriate geneticmeasurements, can then be used to determination the ploidy state of oneor more chromosomes on the individual, and/or the genetic state of oneor a set of alleles, specifically those alleles that are correlated witha disease or genetic state of interest. Note that the use of LIPs hasbeen reported for multiplexed capture of genetic sequences, followed bygenotyping with sequencing. However, the use of sequencing dataresulting from a LIPs-based strategy for the amplification of thegenetic material found in a single cell, a small number of cells, orextracellular DNA, has not been used for the purpose of determining theploidy state of a target individual.

Applying an informatics based method to determine the ploidy state of anindividual from genetic data as measured by hybridization arrays, suchas the ILLUMINA INFINIUM array, or the AFFYMETRIX gene chip has beendescribed in documents references elsewhere in this document. However,the method described herein shows improvements over methods describedpreviously in the literature. For example, the LIPs based approachfollowed by high throughput sequencing unexpectedly provides bettergenotypic data due to the approach having better capacity formultiplexing, better capture specificity, better uniformity, and lowallelic bias. Greater multiplexing allows more alleles to be targeted,giving more accurate results. Better uniformity results in more of thetargeted alleles being measured, giving more accurate results. Lowerrates of allelic bias result in lower rates of miscalls, giving moreaccurate results. More accurate results result in an improvement inclinical outcomes, and better medical care.

It is important to note that LIPs may be used as a method for targetingspecific loci in a sample of DNA for genotyping by methods other thansequencing. For example, LIPs may be used to target DNA for genotypingusing SNP arrays or other DNA or RNA based microarrays.

Ligation-Mediated PCR

Ligation-mediated PCR is method of PCR used to preferentially enrich asample of DNA by amplifying one or a plurality of loci in a mixture ofDNA, the method comprising: obtaining a set of primer pairs, where eachprimer in the pair contains a target specific sequence and a non-targetsequence, where the target specific sequence is designed to anneal to atarget region, one upstream and one downstream from the polymorphicsite, and which can be separated from the polymorphic site by 0, 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11-20, 21-30, 31-40, 41-50, 51-100, or morethan 100; polymerization of the DNA from the 3-prime end of upstreamprimer to the fill the single strand region between it and the 5-primeend of the downstream primer with nucleotides complementary to thetarget molecule; ligation of the last polymerized base of the upstreamprimer to the adjacent 5-prime base of the downstream primer; andamplification of only polymerized and ligated molecules using thenon-target sequences contained at the 5-prime end of the upstream primerand the 3-prime end of the downstream primer. Pairs of primers todistinct targets may be mixed in the same reaction. The non-targetsequences serve as universal sequences such that of all pairs of primersthat have been successfully polymerized and ligated may be amplifiedwith a single pair of amplification primers.

Capture by Hybridization

Preferential enrichment of a specific set of sequences in a targetgenome can be accomplished in a number of ways. Elsewhere in thisdocument is a description of how LIPs can be used to target a specificset of sequences, but in all of those applications, other targetingand/or preferential enrichment methods can be used equally well for thesame ends. One example of another targeting method is the capture byhybridization approach. Some examples of commercial capture byhybridization technologies include AGILENT's SURE SELECT and ILLUMINA'sTRUSEQ. In capture by hybridization, a set of oligonucleotides that iscomplimentary or mostly complimentary to the desired targeted sequencesis allowed to hybridize to a mixture of DNA, and then physicallyseparated from the mixture. Once the desired sequences have hybridizedto the targeting oligonucleotides, the effect of physically removing thetargeting oligonucleotides is to also remove the targeted sequences.Once the hybridized oligos are removed, they can be heated to abovetheir melting temperature and they can be amplified. Some ways tophysically remove the targeting oligonucleotides is by covalentlybonding the targeting oligos to a solid support, for example a magneticbead, or a chip. Another way to physically remove the targetingoligonucleotides is by covalently bonding them to a molecular moietywith a strong affinity for another molecular moiety. An example of sucha molecular pair is biotin and streptavidin, such as is used in SURESELECT. Thus that targeted sequences could be covalently attached to abiotin molecule, and after hybridization, a solid support withstreptavidin affixed can be used to pull down the biotinylatedoligonucleotides, to which are hybridized to the targeted sequences.

Hybrid capture involves hybridizing probes that are complementary to thetargets of interest to the target molecules. Hybrid capture probes wereoriginally developed to target and enrich large fractions of the genomewith relative uniformity between targets. In that application, it wasimportant that all targets be amplified with enough uniformity that allregions could be detected by sequencing, however, no regard was paid toretaining the proportion of alleles in original sample. Followingcapture, the alleles present in the sample can be determined by directsequencing of the captured molecules. These sequencing reads can beanalyzed and counted according the allele type. However, using thecurrent technology, the measured allele distributions the capturedsequences are typically not representative of the original alleledistributions.

In an embodiment, detection of the alleles is performed by sequencing.In order to capture the allele identity at the polymorphic site, it isessential that the sequencing read span the allele in question in orderto evaluate the allelic composition of that captured molecule. Since thecapture molecules are often of variable lengths upon sequencing cannotbe guaranteed to overlap the variant positions unless the entiremolecule is sequenced. However, cost considerations as well as technicallimitations as to the maximum possible length and accuracy of sequencingreads make sequencing the entire molecule unfeasible. In an embodiment,the read length can be increased from about 30 to about 50 or about 70bases can greatly increase the number of reads that overlap the variantpositions within the targeted sequences.

Another way to increase the number of reads that interrogate theposition of interest is to decrease the length of the probe, as long asit does not result in bias in the underlying enriched alleles. Thelength of the synthesized probe should be long enough such that twoprobes designed to hybridize to two different alleles found at one locuswill hybridize with near equal affinity to the various alleles in theoriginal sample. Currently, methods known in the art describe probesthat are typically longer than 120 bases. In a current embodiment, ifthe allele is one or a few bases then the capture probes may be lessthan about 110 bases, less than about 100 bases, less than about 90bases, less than about 80 bases, less than about 70 bases, less thanabout 60 bases, less than about 50 bases, less than about 40 bases, lessthan about 30 bases, and less than about 25 bases, and this issufficient to ensure equal enrichment from all alleles. When the mixtureof DNA that is to be enriched using the hybrid capture technology is amixture comprising free floating DNA isolated from blood, for examplematernal blood, the average length of DNA is quite short, typically lessthan 200 bases. The use of shorter probes results in a greater chancethat the hybrid capture probes will capture desired DNA fragments.Larger variations may require longer probes. In an embodiment, thevariations of interest are one (a SNP) to a few bases in length. In anembodiment, targeted regions in the genome can be preferentiallyenriched using hybrid capture probes wherein the hybrid capture probesare of a length below 90 bases, and can be less than 80 bases, less than70 bases, less than 60 bases, less than 50 bases, less than 40 bases,less than 30 bases, or less than 25 bases. In an embodiment, to increasethe chance that the desired allele is sequenced, the length of the probethat is designed to hybridize to the regions flanking the polymorphicallele location can be decreased from above 90 bases, to about 80 bases,or to about 70 bases, or to about 60 bases, or to about 50 bases, or toabout 40 bases, or to about 30 bases, or to about 25 bases.

There is a minimum overlap between the synthesized probe and the targetmolecule in order to enable capture. This synthesized probe can be madeas short as possible while still being larger than this minimum requiredoverlap. The effect of using a shorter probe length to target apolymorphic region is that there will be more molecules that overlap thetarget allele region. The state of fragmentation of the original DNAmolecules also affects the number of reads that will overlap thetargeted alleles. Some DNA samples such as plasma samples are alreadyfragmented due to biological processes that take place in vivo. However,samples with longer fragments by benefit from fragmentation prior tosequencing library preparation and enrichment. When both probes andfragments are short (˜60-80 bp) maximum specificity may be achievedrelatively few sequence reads failing to overlap the critical region ofinterest.

In an embodiment, the hybridization conditions can be adjusted tomaximize uniformity in the capture of different alleles present in theoriginal sample. In an embodiment, hybridization temperatures aredecreased to minimize differences in hybridization bias between alleles.Methods known in the art avoid using lower temperatures forhybridization because lowering the temperature has the effect ofincreasing hybridization of probes to unintended targets. However, whenthe goal is to preserve allele ratios with maximum fidelity, theapproach of using lower hybridization temperatures provides optimallyaccurate allele ratios, despite the fact that the current art teachesaway from this approach. Hybridization temperature can also be increasedto require greater overlap between the target and the synthesized probeso that only targets with substantial overlap of the targeted region arecaptured. In some embodiments of the present disclosure, thehybridization temperature is lowered from the normal hybridizationtemperature to about 40° C., to about 45° C., to about 50° C., to about55° C., to about 60° C., to about 65, or to about 70° C.

In an embodiment, the hybrid capture probes can be designed such thatthe region of the capture probe with DNA that is complementary to theDNA found in regions flanking the polymorphic allele is not immediatelyadjacent to the polymorphic site. Instead, the capture probe can bedesigned such that the region of the capture probe that is designed tohybridize to the DNA flanking the polymorphic site of the target isseparated from the portion of the capture probe that will be in van derWaals contact with the polymorphic site by a small distance that isequivalent in length to one or a small number of bases. In anembodiment, the hybrid capture probe is designed to hybridize to aregion that is flanking the polymorphic allele but does not cross it;this may be termed a flanking capture probe. The length of the flankingcapture probe may be less than about 120 bases, less than about 110bases, less than about 100 bases, less than about 90 bases, and can beless than about 80 bases, less than about 70 bases, less than about 60bases, less than about 50 bases, less than about 40 bases, less thanabout 30 bases, or less than about 25 bases. The region of the genomethat is targeted by the flanking capture probe may be separated by thepolymorphic locus by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11-20, or more than20 base pairs.

Description of a targeted capture based disease screening test usingtargeted sequence capture. Custom targeted sequence capture, like thosecurrently offered by AGILENT (SURE SELECT), ROCHE-NIMBLEGEN, orILLUMINA. Capture probes could be custom designed to ensure capture ofvarious types of mutations. For point mutations, one or more probes thatoverlap the point mutation should be sufficient to capture and sequencethe mutation.

For small insertions or deletions, one or more probes that overlap themutation may be sufficient to capture and sequence fragments comprisingthe mutation. Hybridization may be less efficient between theprobe-limiting capture efficiency, typically designed to the referencegenome sequence. To ensure capture of fragments comprising the mutationone could design two probes, one matching the normal allele and onematching the mutant allele. A longer probe may enhance hybridization.Multiple overlapping probes may enhance capture. Finally, placing aprobe immediately adjacent to, but not overlapping, the mutation maypermit relatively similar capture efficiency of the normal and mutantalleles.

For Simple Tandem Repeats (STRs), a probe overlapping these highlyvariable sites is unlikely to capture the fragment well. To enhancecapture a probe could be placed adjacent to, but not overlapping thevariable site. The fragment could then be sequenced as normal to revealthe length and composition of the STR.

For large deletions, a series of overlapping probes, a common approachcurrently used in exome capture systems may work. However, with thisapproach it may be difficult to determine whether or not an individualis heterozygous. Targeting and evaluating SNPs within the capturedregion could potentially reveal loss of heterozygosity across the regionindicating that an individual is a carrier. In an embodiment, it ispossible to place non-overlapping or singleton probes across thepotentially deleted region and use the number of fragments captured as ameasure of heterozygosity. In the case where an individual caries alarge deletion, one-half the number of fragments are expected to beavailable for capture relative to a non-deleted (diploid) referencelocus. Consequently, the number of reads obtained from the deletedregions should be roughly half that obtained from a normal diploidlocus. Aggregating and averaging the sequencing read depth from multiplesingleton probes across the potentially deleted region may enhance thesignal and improve confidence of the diagnosis. The two approaches,targeting SNPs to identify loss of heterozygosity and using multiplesingleton probes to obtain a quantitative measure of the quantity ofunderlying fragments from that locus can also be combined. Either orboth of these strategies may be combined with other strategies to betterobtain the same end.

If during testing cfDNA detection of a male fetus, as indicated by thepresence of the Y-chromosome fragments, captured and sequenced in thesame test, and either an X-linked dominant mutation where mother andfather are unaffected, or a dominant mutation where mother is notaffected would indicated heighted risk to the fetus. Detection of twomutant recessive alleles within the same gene in an unaffected motherwould imply the fetus had inherited a mutant allele from father andpotentially a second mutant allele from mother. In all cases, follow-uptesting by amniocentesis or chorionic villus sampling may be indicated.

A targeted capture based disease screening test could be combined with atargeted capture based non-invasive prenatal diagnostic test foraneuploidy.

There are a number of ways to decrease depth of read (DOR) variability:for example, one could increase primer concentrations, one could uselonger targeted amplification probes, or one could run more STA cycles(such as more than 25, more than 30, more than 35, or even more than 40)

Targeted PCR

In some embodiments, PCR can be used to target specific locations of thegenome. In plasma samples, the original DNA is highly fragmented(typically less than 500 bp, with an average length less than 200 bp).In PCR, both forward and reverse primers must anneal to the samefragment to enable amplification. Therefore, if the fragments are short,the PCR assays must amplify relatively short regions as well. Like MIPS,if the polymorphic positions are too close the polymerase binding site,it could result in biases in the amplification from different alleles.Currently, PCR primers that target polymorphic regions, such as thosecontaining SNPs, are typically designed such that the 3′ end of theprimer will hybridize to the base immediately adjacent to thepolymorphic base or bases. In an embodiment of the present disclosure,the 3′ ends of both the forward and reverse PCR primers are designed tohybridize to bases that are one or a few positions away from the variantpositions (polymorphic sites) of the targeted allele. The number ofbases between the polymorphic site (SNP or otherwise) and the base towhich the 3′ end of the primer is designed to hybridize may be one base,it may be two bases, it may be three bases, it may be four bases, it maybe five bases, it may be six bases, it may be seven to ten bases, it maybe eleven to fifteen bases, or it may be sixteen to twenty bases. Theforward and reverse primers may be designed to hybridize a differentnumber of bases away from the polymorphic site.

PCR assay can be generated in large numbers, however, the interactionsbetween different PCR assays makes it difficult to multiplex them beyondabout one hundred assays. Various complex molecular approaches can beused to increase the level of multiplexing, but it may still be limitedto fewer than 100, perhaps 200, or possibly 500 assays per reaction.Samples with large quantities of DNA can be split among multiplesub-reactions and then recombined before sequencing. For samples whereeither the overall sample or some subpopulation of DNA molecules islimited, splitting the sample would introduce statistical noise. In anembodiment, a small or limited quantity of DNA may refer to an amountbelow 10 pg, between 10 and 100 pg, between 100 pg and 1 ng, between 1and 10 ng, or between 10 and 100 ng. Note that while this method isparticularly useful on small amounts of DNA where other methods thatinvolve splitting into multiple pools can cause significant problemsrelated to introduced stochastic noise, this method still provides thebenefit of minimizing bias when it is run on samples of any quantity ofDNA. In these situations, a universal pre-amplification step may be usedto increase the overall sample quantity. Ideally, this pre-amplificationstep should not appreciably alter the allelic distributions.

In an embodiment, a method of the present disclosure can generate PCRproducts that are specific to a large number of targeted loci,specifically 1,000 to 5,000 loci, 5,000 to 10,000 loci or more than10,000 loci, for genotyping by sequencing or some other genotypingmethod, from limited samples such as single cells or DNA from bodyfluids. Currently, performing multiplex PCR reactions of more than 5 to10 targets presents a major challenge and is often hindered by primerside products, such as primer dimers, and other artifacts. Whendetecting target sequences using microarrays with hybridization probes,primer dimers and other artifacts may be ignored, as these are notdetected. However, when using sequencing as a method of detection, thevast majority of the sequencing reads would sequence such artifacts andnot the desired target sequences in a sample. Methods described in theprior art used to multiplex more than 50 or 100 reactions in onereaction followed by sequencing will typically result in more than 20%,and often more than 50%, in many cases more than 80% and in some casesmore than 90% off-target sequence reads.

In general, to perform targeted sequencing of multiple (n) targets of asample (greater than 50, greater than 100, greater than 500, or greaterthan 1,000), one can split the sample into a number of parallelreactions that amplify one individual target. This has been performed inPCR multiwell plates or can be done in commercial platforms such as theFLUIDIGM ACCESS ARRAY (48 reactions per sample in microfluidic chips) orDROPLET PCR by RAIN DANCE TECHNOLOGY (100s to a few thousands oftargets). Unfortunately, these split-and-pool methods are problematicfor samples with a limited amount of DNA, as there is often not enoughcopies of the genome to ensure that there is one copy of each region ofthe genome in each well. This is an especially severe problem whenpolymorphic loci are targeted, and the relative proportions of thealleles at the polymorphic loci are needed, as the stochastic noiseintroduced by the splitting and pooling will cause very poorly accuratemeasurements of the proportions of the alleles that were present in theoriginal sample of DNA. Described here is a method to effectively andefficiently amplify many PCR reactions that is applicable to cases whereonly a limited amount of DNA is available. In an embodiment, the methodmay be applied for analysis of single cells, body fluids, mixtures ofDNA such as the free floating DNA found in maternal plasma, biopsies,environmental and/or forensic samples.

In an embodiment, the targeted sequencing may involve one, a plurality,or all of the following steps. a) Generate and amplify a library withadaptor sequences on both ends of DNA fragments. b) Divide into multiplereactions after library amplification. c) Generate and optionallyamplify a library with adaptor sequences on both ends of DNA fragments.d) Perform 1000- to 10,000-plex amplification of selected targets usingone target specific “Forward” primer per target and one tag specificprimer. e) Perform a second amplification from this product using“Reverse” target specific primers and one (or more) primer specific to auniversal tag that was introduced as part of the target specific forwardprimers in the first round. f) Perform a 1000-plex preamplification ofselected target for a limited number of cycles. g) Divide the productinto multiple aliquots and amplify subpools of targets in individualreactions (for example, 50 to 500-plex, though this can be used all theway down to singleplex. h) Pool products of parallel subpools reactions.i) During these amplifications primers may carry sequencing compatibletags (partial or full length) such that the products can be sequenced.

Highly Multiplexed PCR

Disclosed herein are methods that permit the targeted amplification ofover a hundred to tens of thousands of target sequences (e.g. SNP loci)from genomic DNA obtained from plasma. The amplified sample may berelatively free of primer dimer products and have low allelic bias attarget loci. If during or after amplification the products are appendedwith sequencing compatible adaptors, analysis of these products can beperformed by sequencing.

Performing a highly multiplexed PCR amplification using methods known inthe art results in the generation of primer dimer products that are inexcess of the desired amplification products and not suitable forsequencing. These can be reduced empirically by eliminating primers thatform these products, or by performing in silico selection of primers.However, the larger the number of assays, the more difficult thisproblem becomes.

One solution is to split the 5000-plex reaction into severallower-plexed amplifications, e.g. one hundred 50-plex or fifty 100-plexreactions, or to use microfluidics or even to split the sample intoindividual PCR reactions. However, if the sample DNA is limited, such asin non-invasive prenatal diagnostics from pregnancy plasma, dividing thesample between multiple reactions should be avoided as this will resultin bottlenecking.

Described herein are methods to first globally amplify the plasma DNA ofa sample and then divide the sample up into multiple multiplexed targetenrichment reactions with more moderate numbers of target sequences perreaction. In an embodiment, a method of the present disclosure can beused for preferentially enriching a DNA mixture at a plurality of loci,the method comprising one or more of the following steps: generating andamplifying a library from a mixture of DNA where the molecules in thelibrary have adaptor sequences ligated on both ends of the DNAfragments, dividing the amplified library into multiple reactions,performing a first round of multiplex amplification of selected targetsusing one target specific “forward” primer per target and one or aplurality of adaptor specific universal “reverse” primers. In anembodiment, a method of the present disclosure further includesperforming a second amplification using “reverse” target specificprimers and one or a plurality of primers specific to a universal tagthat was introduced as part of the target specific forward primers inthe first round. In an embodiment, the method may involve a fullynested, hemi-nested, semi-nested, one sided fully nested, one sidedhemi-nested, or one sided semi-nested PCR approach. In an embodiment, amethod of the present disclosure is used for preferentially enriching aDNA mixture at a plurality of loci, the method comprising performing amultiplex preamplification of selected targets for a limited number ofcycles, dividing the product into multiple aliquots and amplifyingsubpools of targets in individual reactions, and pooling products ofparallel subpools reactions. Note that this approach could be used toperform targeted amplification in a manner that would result in lowlevels of allelic bias for 50-500 loci, for 500 to 5,000 loci, for 5,000to 50,000 loci, or even for 50,000 to 500,000 loci. In an embodiment,the primers carry partial or full length sequencing compatible tags.

The workflow may entail (1) extracting plasma DNA, (2) preparingfragment library with universal adaptors on both ends of fragments, (3)amplifying the library using universal primers specific to the adaptors,(4) dividing the amplified sample “library” into multiple aliquots, (5)performing multiplex (e.g. about 100-plex, 1,000, or 10,000-plex withone target specific primer per target and a tag-specific primer)amplifications on aliquots, (6) pooling aliquots of one sample, (7)barcoding the sample, (8) mixing the samples and adjusting theconcentration, (9) sequencing the sample. The workflow may comprisemultiple sub-steps that contain one of the listed steps (e.g. step (2)of preparing the library step could entail three enzymatic steps (bluntending, dA tailing and adaptor ligation) and three purification steps).Steps of the workflow may be combined, divided up or performed indifferent order (e.g. bar coding and pooling of samples).

It is important to note that the amplification of a library can beperformed in such a way that it is biased to amplify short fragmentsmore efficiently. In this manner it is possible to preferentiallyamplify shorter sequences, e.g. mono-nucleosomal DNA fragments as thecell free fetal DNA (of placental origin) found in the circulation ofpregnant women. Note that PCR assays can have the tags, for examplesequencing tags, (usually a truncated form of 15-25 bases). Aftermultiplexing, PCR multiplexes of a sample are pooled and then the tagsare completed (including bar coding) by a tag-specific PCR (could alsobe done by ligation). Also, the full sequencing tags can be added in thesame reaction as the multiplexing. In the first cycles targets may beamplified with the target specific primers, subsequently thetag-specific primers take over to complete the SQ-adaptor sequence. ThePCR primers may carry no tags. The sequencing tags may be appended tothe amplification products by ligation.

In an embodiment, highly multiplex PCR followed by evaluation ofamplified material by clonal sequencing may be used to detect fetalaneuploidy. Whereas traditional multiplex PCRs evaluate up to fifty locisimultaneously, the approach described herein may be used to enablesimultaneous evaluation of more than 50 loci simultaneously, more than100 loci simultaneously, more than 500 loci simultaneously, more than1,000 loci simultaneously, more than 5,000 loci simultaneously, morethan 10,000 loci simultaneously, more than 50,000 loci simultaneously,and more than 100,000 loci simultaneously. Experiments have shown thatup to, including and more than 10,000 distinct loci can be evaluatedsimultaneously, in a single reaction, with sufficiently good efficiencyand specificity to make non-invasive prenatal aneuploidy diagnosesand/or copy number calls with high accuracy. Assays may be combined in asingle reaction with the entirety of a cfDNA sample isolated frommaternal plasma, a fraction thereof, or a further processed derivativeof the cfDNA sample. The cfDNA or derivative may also be split intomultiple parallel multiplex reactions. The optimum sample splitting andmultiplex is determined by trading off various performancespecifications. Due to the limited amount of material, splitting thesample into multiple fractions can introduce sampling noise, handlingtime, and increase the possibility of error. Conversely, highermultiplexing can result in greater amounts of spurious amplification andgreater inequalities in amplification both of which can reduce testperformance.

Two crucial related considerations in the application of the methodsdescribed herein are the limited amount of original plasma and thenumber of original molecules in that material from which allelefrequency or other measurements are obtained. If the number of originalmolecules falls below a certain level, random sampling noise becomessignificant, and can affect the accuracy of the test. Typically, data ofsufficient quality for making non-invasive prenatal aneuploidy diagnosescan be obtained if measurements are made on a sample comprising theequivalent of 500-1000 original molecules per target locus. There are anumber of ways of increasing the number of distinct measurements, forexample increasing the sample volume. Each manipulation applied to thesample also potentially results in losses of material. It is essentialto characterize losses incurred by various manipulations and avoid, oras necessary improve yield of certain manipulations to avoid losses thatcould degrade performance of the test.

In an embodiment, it is possible to mitigate potential losses insubsequent steps by amplifying all or a fraction of the original cfDNAsample. Various methods are available to amplify all of the geneticmaterial in a sample, increasing the amount available for downstreamprocedures. In an embodiment, ligation mediated PCR (LM-PCR) DNAfragments are amplified by PCR after ligation of either one distinctadaptors, two distinct adapters, or many distinct adaptors. In anembodiment, multiple displacement amplification (MDA) phi-29 polymeraseis used to amplify all DNA isothermally. In DOP-PCR and variations,random priming is used to amplify the original material DNA. Each methodhas certain characteristics such as uniformity of amplification acrossall represented regions of the genome, efficiency of capture andamplification of original DNA, and amplification performance as afunction of the length of the fragment.

In an embodiment LM-PCR may be used with a single heteroduplexed adaptorhaving a 3-prime tyrosine. The heteroduplexed adaptor enables the use ofa single adaptor molecule that may be converted to two distinctsequences on 5-prime and 3-prime ends of the original DNA fragmentduring the first round of PCR. In an embodiment, it is possible tofractionate the amplified library by size separations, or products suchas AMPURE, TASS or other similar methods. Prior to ligation, sample DNAmay be blunt ended, and then a single adenosine base is added to the3-prime end. Prior to ligation the DNA may be cleaved using arestriction enzyme or some other cleavage method. During ligation the3-prime adenosine of the sample fragments and the complementary 3-primetyrosine overhang of adaptor can enhance ligation efficiency. Theextension step of the PCR amplification may be limited from a timestandpoint to reduce amplification from fragments longer than about 200bp, about 300 bp, about 400 bp, about 500 bp or about 1,000 bp. Sincelonger DNA found in the maternal plasma is nearly exclusively maternal,this may result in the enrichment of fetal DNA by 10-50% and improvementof test performance. A number of reactions were run using conditions asspecified by commercially available kits; the resulted in successfulligation of fewer than 10% of sample DNA molecules. A series ofoptimizations of the reaction conditions for this improved ligation toapproximately 70%.

Mini-PCR

Traditional PCR assay design results in significant losses of distinctfetal molecules, but losses can be greatly reduced by designing veryshort PCR assays, termed mini-PCR assays. Fetal cfDNA in maternal serumis highly fragmented and the fragment sizes are distributed inapproximately a Gaussian fashion with a mean of 160 bp, a standarddeviation of 15 bp, a minimum size of about 100 bp, and a maximum sizeof about 220 bp. The distribution of fragment start and end positionswith respect to the targeted polymorphisms, while not necessarilyrandom, vary widely among individual targets and among all targetscollectively and the polymorphic site of one particular target locus mayoccupy any position from the start to the end among the variousfragments originating from that locus. Note that the term mini-PCR mayequally well refer to normal PCR with no additional restrictions orlimitations.

During PCR, amplification will only occur from template DNA fragmentscomprising both forward and reverse primer sites. Because fetal cfDNAfragments are short, the likelihood of both primer sites being presentthe likelihood of a fetal fragment of length L comprising both theforward and reverse primers sites is ratio of the length of the ampliconto the length of the fragment. Under ideal conditions, assays in whichthe amplicon is 45, 50, 55, 60, 65, or 70 bp will successfully amplifyfrom 72%, 69%, 66%, 63%, 59%, or 56%, respectively, of availabletemplate fragment molecules. The amplicon length is the distance betweenthe 5-prime ends of the forward and reverse priming sites. Ampliconlength that is shorter than typically used by those known in the art mayresult in more efficient measurements of the desired polymorphic loci byonly requiring short sequence reads. In an embodiment, a substantialfraction of the amplicons should be less than 100 bp, less than 90 bp,less than 80 bp, less than 70 bp, less than 65 bp, less than 60 bp, lessthan 55 bp, less than 50 bp, or less than 45 bp.

Note that in methods known in the prior art, short assays such as thosedescribed herein are usually avoided because they are not required andthey impose considerable constraint on primer design by limiting primerlength, annealing characteristics, and the distance between the forwardand reverse primer.

Also note that there is the potential for biased amplification if the3-prime end of the either primer is within roughly 1-6 bases of thepolymorphic site. This single base difference at the site of initialpolymerase binding can result in preferential amplification of oneallele, which can alter observed allele frequencies and degradeperformance. All of these constraints make it very challenging toidentify primers that will amplify a particular locus successfully andfurthermore, to design large sets of primers that are compatible in thesame multiplex reaction. In an embodiment, the 3′ end of the innerforward and reverse primers are designed to hybridize to a region of DNAupstream from the polymorphic site, and separated from the polymorphicsite by a small number of bases. Ideally, the number of bases may bebetween 6 and 10 bases, but may equally well be between 4 and 15 bases,between three and 20 bases, between two and 30 bases, or between 1 and60 bases, and achieve substantially the same end.

Multiplex PCR may involve a single round of PCR in which all targets areamplified or it may involve one round of PCR followed by one or morerounds of nested PCR or some variant of nested PCR. Nested PCR consistsof a subsequent round or rounds of PCR amplification using one or morenew primers that bind internally, by at least one base pair, to theprimers used in a previous round. Nested PCR reduces the number ofspurious amplification targets by amplifying, in subsequent reactions,only those amplification products from the previous one that have thecorrect internal sequence. Reducing spurious amplification targetsimproves the number of useful measurements that can be obtained,especially in sequencing. Nested PCR typically entails designing primerscompletely internal to the previous primer binding sites, necessarilyincreasing the minimum DNA segment size required for amplification. Forsamples such as maternal plasma cfDNA, in which the DNA is highlyfragmented, the larger assay size reduces the number of distinct cfDNAmolecules from which a measurement can be obtained. In an embodiment, tooffset this effect, one may use a partial nesting approach where one orboth of the second round primers overlap the first binding sitesextending internally some number of bases to achieve additionalspecificity while minimally increasing in the total assay size.

In an embodiment, a multiplex pool of PCR assays are designed to amplifypotentially heterozygous SNP or other polymorphic or non-polymorphicloci on one or more chromosomes and these assays are used in a singlereaction to amplify DNA. The number of PCR assays may be between 50 and200 PCR assays, between 200 and 1,000 PCR assays, between 1,000 and5,000 PCR assays, or between 5,000 and 20,000 PCR assays (50 to200-plex, 200 to 1,000-plex, 1,000 to 5,000-plex, 5,000 to 20,000-plex,more than 20,000-plex respectively). In an embodiment, a multiplex poolof about 10,000 PCR assays (10,000-plex) are designed to amplifypotentially heterozygous SNP loci on chromosomes X, Y, 13, 18, and 21and 1 or 2 and these assays are used in a single reaction to amplifycfDNA obtained from a material plasma sample, chorion villus samples,amniocentesis samples, single or a small number of cells, other bodilyfluids or tissues, cancers, or other genetic matter. The SNP frequenciesof each locus may be determined by clonal or some other method ofsequencing of the amplicons. Statistical analysis of the allelefrequency distributions or ratios of all assays may be used to determineif the sample contains a trisomy of one or more of the chromosomesincluded in the test. In another embodiment the original cfDNA samplesis split into two samples and parallel 5,000-plex assays are performed.In another embodiment the original cfDNA samples is split into n samplesand parallel (10,000/n)-plex assays are performed where n is between 2and 12, or between 12 and 24, or between 24 and 48, or between 48 and96. Data is collected and analyzed in a similar manner to that alreadydescribed. Note that this method is equally well applicable to detectingtranslocations, deletions, duplications, and other chromosomalabnormalities.

In an embodiment, tails with no homology to the target genome may alsobe added to the 3-prime or 5-prime end of any of the primers. Thesetails facilitate subsequent manipulations, procedures, or measurements.In an embodiment, the tail sequence can be the same for the forward andreverse target specific primers. In an embodiment, different tails maybe used for the forward and reverse target specific primers. In anembodiment, a plurality of different tails may be used for differentloci or sets of loci. Certain tails may be shared among all loci oramong subsets of loci. For example, using forward and reverse tailscorresponding to forward and reverse sequences required by any of thecurrent sequencing platforms can enable direct sequencing followingamplification. In an embodiment, the tails can be used as common primingsites among all amplified targets that can be used to add other usefulsequences. In some embodiments, the inner primers may contain a regionthat is designed to hybridize either upstream or downstream of thetargeted polymorphic locus. In some embodiments, the primers may containa molecular barcode. In some embodiments, the primer may contain auniversal priming sequence designed to allow PCR amplification.

In an embodiment, a 10,000-plex PCR assay pool is created such thatforward and reverse primers have tails corresponding to the requiredforward and reverse sequences required by a high throughput sequencinginstrument such as the HISEQ, GAIIX, or MYSEQ available from ILLUMINA.In addition, included 5-prime to the sequencing tails is an additionalsequence that can be used as a priming site in a subsequent PCR to addnucleotide barcode sequences to the amplicons, enabling multiplexsequencing of multiple samples in a single lane of the high throughputsequencing instrument.

In an embodiment, a 10,000-plex PCR assay pool is created such thatreverse primers have tails corresponding to the required reversesequences required by a high throughput sequencing instrument. Afteramplification with the first 10,000-plex assay, a subsequent PCRamplification may be performed using a another 10,000-plex pool havingpartly nested forward primers (e.g. 6-bases nested) for all targets anda reverse primer corresponding to the reverse sequencing tail includedin the first round. This subsequent round of partly nested amplificationwith just one target specific primer and a universal primer limits therequired size of the assay, reducing sampling noise, but greatly reducesthe number of spurious amplicons. The sequencing tags can be added toappended ligation adaptors and/or as part of PCR probes, such that thetag is part of the final amplicon.

Fetal fraction affects performance of the test. There are a number ofways to enrich the fetal fraction of the DNA found in maternal plasma.Fetal fraction can be increased by the previously described LM-PCRmethod already discussed as well as by a targeted removal of longmaternal fragments. In an embodiment, prior to multiplex PCRamplification of the target loci, an additional multiplex PCR reactionmay be carried out to selectively remove long and largely maternalfragments corresponding to the loci targeted in the subsequent multiplexPCR. Additional primers are designed to anneal a site a greater distancefrom the polymorphism than is expected to be present among cell freefetal DNA fragments. These primers may be used in a one cycle multiplexPCR reaction prior to multiplex PCR of the target polymorphic loci.These distal primers are tagged with a molecule or moiety that can allowselective recognition of the tagged pieces of DNA. In an embodiment,these molecules of DNA may be covalently modified with a biotin moleculethat allows removal of newly formed double stranded DNA comprising theseprimers after one cycle of PCR. Double stranded DNA formed during thatfirst round is likely maternal in origin. Removal of the hybrid materialmay be accomplished by the used of magnetic streptavidin beads. Thereare other methods of tagging that may work equally well. In anembodiment, size selection methods may be used to enrich the sample forshorter strands of DNA; for example, those less than about 800 bp, lessthan about 500 bp, or less than about 300 bp. Amplification of shortfragments can then proceed as usual.

The mini-PCR method described in this disclosure enables highlymultiplexed amplification and analysis of hundreds to thousands or evenmillions of loci in a single reaction, from a single sample. At thesame, the detection of the amplified DNA can be multiplexed; tens tohundreds of samples can be multiplexed in one sequencing lane by usingbarcoding PCR. This multiplexed detection has been successfully testedup to 49-plex, and a much higher degree of multiplexing is possible. Ineffect, this allows hundreds of samples to be genotyped at thousands ofSNPs in a single sequencing run. For these samples, the method allowsdetermination of genotype and heterozygosity rate and simultaneouslydetermination of copy number, both of which may be used for the purposeof aneuploidy detection. This method is particularly useful in detectinganeuploidy of a gestating fetus from the free floating DNA found inmaternal plasma. This method may be used as part of a method for sexinga fetus, and/or predicting the paternity of the fetus. It may be used aspart of a method for mutation dosage. This method may be used for anyamount of DNA or RNA, and the targeted regions may be SNPs, otherpolymorphic regions, non-polymorphic regions, and combinations thereof.

In some embodiments, ligation mediated universal-PCR amplification offragmented DNA may be used. The ligation mediated universal-PCRamplification can be used to amplify plasma DNA, which can then bedivided into multiple parallel reactions. It may also be used topreferentially amplify short fragments, thereby enriching fetalfraction. In some embodiments the addition of tags to the fragments byligation can enable detection of shorter fragments, use of shortertarget sequence specific portions of the primers and/or annealing athigher temperatures which reduces unspecific reactions.

The methods described herein may be used for a number of purposes wherethere is a target set of DNA that is mixed with an amount ofcontaminating DNA. In some embodiments, the target DNA and thecontaminating DNA may be from individuals who are genetically related.For example, genetic abnormalities in a fetus (target) may be detectedfrom maternal plasma which contains fetal (target) DNA and also maternal(contaminating) DNA; the abnormalities include whole chromosomeabnormalities (e.g. aneuploidy) partial chromosome abnormalities (e.g.deletions, duplications, inversions, translocations), polynucleotidepolymorphisms (e.g. STRs), single nucleotide polymorphisms, and/or othergenetic abnormalities or differences. In some embodiments, the targetand contaminating DNA may be from the same individual, but where thetarget and contaminating DNA are different by one or more mutations, forexample in the case of cancer. (see e.g. H. Mamon et al. PreferentialAmplification of Apoptotic DNA from Plasma: Potential for EnhancingDetection of Minor DNA Alterations in Circulating DNA. ClinicalChemistry 54:9 (2008). In some embodiments, the DNA may be found in cellculture (apoptotic) supernatant. In some embodiments, it is possible toinduce apoptosis in biological samples (e.g. blood) for subsequentlibrary preparation, amplification and/or sequencing. A number ofenabling workflows and protocols to achieve this end are presentedelsewhere in this disclosure.

In some embodiments, the target DNA may originate from single cells,from samples of DNA consisting of less than one copy of the targetgenome, from low amounts of DNA, from DNA from mixed origin (e.g.pregnancy plasma: placental and maternal DNA; cancer patient plasma andtumors: mix between healthy and cancer DNA, transplantation etc), fromother body fluids, from cell cultures, from culture supernatants, fromforensic samples of DNA, from ancient samples of DNA (e.g. insectstrapped in amber), from other samples of DNA, and combinations thereof.

In some embodiments, a short amplicon size may be used. Short ampliconsizes are especially suited for fragmented DNA (see e.g. A. Sikora, etsl. Detection of increased amounts of cell-free fetal DNA with short PCRamplicons. Clin Chem. 2010 January; 56(1):136-8.)

The use of short amplicon sizes may result in some significant benefits.Short amplicon sizes may result in optimized amplification efficiency.Short amplicon sizes typically produce shorter products, therefore thereis less chance for nonspecific priming. Shorter products can beclustered more densely on sequencing flow cell, as the clusters will besmaller. Note that the methods described herein may work equally wellfor longer PCR amplicons. Amplicon length may be increased if necessary,for example, when sequencing larger sequence stretches. Experiments with146-plex targeted amplification with assays of 100 bp to 200 bp lengthas first step in a nested-PCR protocol were run on single cells and ongenomic DNA with positive results.

In some embodiments, the methods described herein may be used to amplifyand/or detect SNPs, copy number, nucleotide methylation, mRNA levels,other types of RNA expression levels, other genetic and/or epigeneticfeatures. The mini-PCR methods described herein may be used along withnext-generation sequencing; it may be used with other downstream methodssuch as microarrays, counting by digital PCR, real-time PCR,Mass-spectrometry analysis etc.

In some embodiment, the mini-PCR amplification methods described hereinmay be used as part of a method for accurate quantification of minoritypopulations. It may be used for absolute quantification using spikecalibrators. It may be used for mutation/minor allele quantificationthrough very deep sequencing, and may be run in a highly multiplexedfashion. It may be used for standard paternity and identity testing ofrelatives or ancestors, in human, animals, plants or other creatures. Itmay be used for forensic testing. It may be used for rapid genotypingand copy number analysis (CN), on any kind of material, e.g. amnioticfluid and CVS, sperm, product of conception (POC). It may be used forsingle cell analysis, such as genotyping on samples biopsied fromembryos. It may be used for rapid embryo analysis (within less than one,one, or two days of biopsy) by targeted sequencing using min-PCR.

In some embodiments, it may be used for tumor analysis: tumor biopsiesare often a mixture of health and tumor cells. Targeted PCR allows deepsequencing of SNPs and loci with close to no background sequences. Itmay be used for copy number and loss of heterozygosity analysis on tumorDNA. Said tumor DNA may be present in many different body fluids ortissues of tumor patients. It may be used for detection of tumorrecurrence, and/or tumor screening. It may be used for quality controltesting of seeds. It may be used for breeding, or fishing purposes. Notethat any of these methods could equally well be used targetingnon-polymorphic loci for the purpose of ploidy calling.

Some literature describing some of the fundamental methods that underliethe methods disclosed herein include: (1) Wang H Y, Luo M, TereshchenkoI V, Frikker D M, Cui X, Li J Y, Hu G, Chu Y, Azaro M A, Lin Y, Shen L,Yang Q, Kambouris M E, Gao R, Shih W, Li H. Genome Res. 2005 February;15(2):276-83. Department of Molecular Genetics, Microbiology andImmunology/The Cancer Institute of New Jersey, Robert Wood JohnsonMedical School, New Brunswick, N.J. 08903, USA. (2) High-throughputgenotyping of single nucleotide polymorphisms with high sensitivity. LiH, Wang H Y, Cui X, Luo M, Hu G, Greenawalt D M, Tereshchenko I V, Li JY, Chu Y, Gao R. Methods Mol Biol. 2007; 396—PubMed PMID: 18025699. (3)A method comprising multiplexing of an average of 9 assays forsequencing is described in: Nested Patch PCR enables highly multiplexedmutation discovery in candidate genes. Varley K E, Mitra R D. GenomeRes. 2008 November; 18(11):1844-50. Epub 2008 Oct. 10. Note that themethods disclosed herein allow multiplexing of orders of magnitude morethan in the above references.

Primer Design

Highly multiplexed PCR can often result in the production of a very highproportion of product DNA that results from unproductive side reactionssuch as primer dimer formation. In an embodiment, the particular primersthat are most likely to cause unproductive side reactions may be removedfrom the primer library to give a primer library that will result in agreater proportion of amplified DNA that maps to the genome. The step ofremoving problematic primers, that is, those primers that areparticularly likely to firm dimers has unexpectedly enabled extremelyhigh PCR multiplexing levels for subsequent analysis by sequencing. Insystems such as sequencing, where performance significantly degrades byprimer dimers and/or other mischief products, greater than 10, greaterthan 50, and greater than 100 times higher multiplexing than otherdescribed multiplexing has been achieved. Note this is opposed to probebased detection methods, e.g. microarrays, TAQMAN, PCR etc. where anexcess of primer dimers will not affect the outcome appreciably. Alsonote that the general belief in the art is that multiplexing PCR forsequencing is limited to about 100 assays in the same well. E.g.Fluidigm and Rain Dance offer platforms to perform 48 or 1000s of PCRassays in parallel reactions for one sample.

There are a number of ways to choose primers for a library where theamount of non-mapping primer-dimer or other primer mischief products areminimized. Empirical data indicate that a small number of ‘bad’ primersare responsible for a large amount of non-mapping primer dimer sidereactions. Removing these ‘bad’ primers can increase the percent ofsequence reads that map to targeted loci. One way to identify the ‘bad’primers is to look at the sequencing data of DNA that was amplified bytargeted amplification; those primer dimers that are seen with greatestfrequency can be removed to give a primer library that is significantlyless likely to result in side product DNA that does not map to thegenome. There are also publicly available programs that can calculatethe binding energy of various primer combinations, and removing thosewith the highest binding energy will also give a primer library that issignificantly less likely to result in side product DNA that does notmap to the genome.

Multiplexing large numbers of primers imposes considerable constraint onthe assays that can be included. Assays that unintentionally interactresult in spurious amplification products. The size constraints ofminiPCR may result in further constraints. In an embodiment, it ispossible to begin with a very large number of potential SNP targets(between about 500 to greater than 1 million) and attempt to designprimers to amplify each SNP. Where primers can be designed it ispossible to attempt to identify primer pairs likely to form spuriousproducts by evaluating the likelihood of spurious primer duplexformation between all possible pairs of primers using publishedthermodynamic parameters for DNA duplex formation. Primer interactionsmay be ranked by a scoring function related to the interaction andprimers with the worst interaction scores are eliminated until thenumber of primers desired is met. In cases where SNPs likely to beheterozygous are most useful, it is possible to also rank the list ofassays and select the most heterozygous compatible assays. Experimentshave validated that primers with high interaction scores are most likelyto form primer dimers. At high multiplexing it is not possible toeliminate all spurious interactions, but it is essential to remove theprimers or pairs of primers with the highest interaction scores insilico as they can dominate an entire reaction, greatly limitingamplification from intended targets. We have performed this procedure tocreate multiplex primer sets of up 10,000 primers. The improvement dueto this procedure is substantial, enabling amplification of more than80%, more than 90%, more than 95%, more than 98%, and even more than 99%on target products as determined by sequencing of all PCR products, ascompared to 10% from a reaction in which the worst primers were notremoved. When combined with a partial semi-nested approach as previouslydescribed, more than 90%, and even more than 95% of amplicons may map tothe targeted sequences.

Note that there are other methods for determining which PCR probes arelikely to form dimers. In an embodiment, analysis of a pool of DNA thathas been amplified using a non-optimized set of primers may besufficient to determine problematic primers. For example, analysis maybe done using sequencing, and those dimers which are present in thegreatest number are determined to be those most likely to form dimers,and may be removed.

This method has a number of potential application, for example to SNPgenotyping, heterozygosity rate determination, copy number measurement,and other targeted sequencing applications. In an embodiment, the methodof primer design may be used in combination with the mini-PCR methoddescribed elsewhere in this document. In some embodiments, the primerdesign method may be used as part of a massive multiplexed PCR method.

The use of tags on the primers may reduce amplification and sequencingof primer dimer products. Tag-primers can be used to shorten necessarytarget-specific sequence to below 20, below 15, below 12, and even below10 base pairs. This can be serendipitous with standard primer designwhen the target sequence is fragmented within the primer binding siteor, or it can be designed into the primer design. Advantages of thismethod include: it increases the number of assays that can be designedfor a certain maximal amplicon length, and it shortens the“non-informative” sequencing of primer sequence. It may also be used incombination with internal tagging (see elsewhere in this document).

In an embodiment, the relative amount of nonproductive products in themultiplexed targeted PCR amplification can be reduced by raising theannealing temperature. In cases where one is amplifying libraries withthe same tag as the target specific primers, the annealing temperaturecan be increased in comparison to the genomic DNA as the tags willcontribute to the primer binding. In some embodiments we are usingconsiderably lower primer concentrations than previously reported alongwith using longer annealing times than reported elsewhere. In someembodiments the annealing times may be longer than 10 minutes, longerthan 20 minutes, longer than 30 minutes, longer than 60 minutes, longerthan 120 minutes, longer than 240 minutes, longer than 480 minutes, andeven longer than 960 minutes. In an embodiment, longer annealing timesare used than in previous reports, allowing lower primer concentrations.In some embodiments, the primer concentrations are as low as 50 nM, 20nM, 10 nM, 5 nM, 1 nM, and lower than 1 uM. This surprisingly results inrobust performance for highly multiplexed reactions, for example1,000-plex reactions, 2,000-plex reactions, 5,000-plex reactions,10,000-plex reactions, 20,000-plex reactions, 50,000-plex reactions, andeven 100,000-plex reactions. In an embodiment, the amplification usesone, two, three, four or five cycles run with long annealing times,followed by PCR cycles with more usual annealing times with taggedprimers.

To select target locations, one may start with a pool of candidateprimer pair designs and create a thermodynamic model of potentiallyadverse interactions between primer pairs, and then use the model toeliminate designs that are incompatible with other the designs in thepool.

Targeted PCR Variants—Nesting

There are many workflows that are possible when conducting PCR; someworkflows typical to the methods disclosed herein are described. Thesteps outlined herein are not meant to exclude other possible steps nordoes it imply that any of the steps described herein are required forthe method to work properly. A large number of parameter variations orother modifications are known in the literature, and may be made withoutaffecting the essence of the invention. One particular generalizedworkflow is given below followed by a number of possible variants. Thevariants typically refer to possible secondary PCR reactions, forexample different types of nesting that may be done (step 3). It isimportant to note that variants may be done at different times, or indifferent orders than explicitly described herein.

1. The DNA in the sample may have ligation adapters, often referred toas library tags or ligation adaptor tags (LTs), appended, where theligation adapters contain a universal priming sequence, followed by auniversal amplification. In an embodiment, this may be done using astandard protocol designed to create sequencing libraries afterfragmentation. In an embodiment, the DNA sample can be blunt ended, andthen an A can be added at the 3′ end. A Y-adaptor with a T-overhang canbe added and ligated. In some embodiments, other sticky ends can be usedother than an A or T overhang. In some embodiments, other adaptors canbe added, for example looped ligation adaptors. In some embodiments, theadaptors may have tag designed for PCR amplification.2. Specific Target Amplification (STA): Pre-amplification of hundreds tothousands to tens of thousands and even hundreds of thousands of targetsmay be multiplexed in one reaction. STA is typically run from 10 to 30cycles, though it may be run from 5 to 40 cycles, from 2 to 50 cycles,and even from 1 to 100 cycles. Primers may be tailed, for example for asimpler workflow or to avoid sequencing of a large proportion of dimers.Note that typically, dimers of both primers carrying the same tag willnot be amplified or sequenced efficiently. In some embodiments, between1 and 10 cycles of PCR may be carried out; in some embodiments between10 and 20 cycles of PCR may be carried out; in some embodiments between20 and 30 cycles of PCR may be carried out; in some embodiments between30 and 40 cycles of PCR may be carried out; in some embodiments morethan 40 cycles of PCR may be carried out. The amplification may be alinear amplification. The number of PCR cycles may be optimized toresult in an optimal depth of read (DOR) profile. Different DOR profilesmay be desirable for different purposes. In some embodiments, a moreeven distribution of reads between all assays is desirable; if the DORis too small for some assays, the stochastic noise can be too high forthe data to be too useful, while if the depth of read is too high, themarginal usefulness of each additional read is relatively small.

Primer tails may improve the detection of fragmented DNA fromuniversally tagged libraries. If the library tag and the primer-tailscontain a homologous sequence, hybridization can be improved (forexample, melting temperature (T_(M)) is lowered) and primers can beextended if only a portion of the primer target sequence is in thesample DNA fragment. In some embodiments, 13 or more target specificbase pairs may be used. In some embodiments, 10 to 12 target specificbase pairs may be used. In some embodiments, 8 to 9 target specific basepairs may be used. In some embodiments, 6 to 7 target specific basepairs may be used. In some embodiments, STA may be performed onpre-amplified DNA, e.g. MDA, RCA, other whole genome amplifications, oradaptor-mediated universal PCR. In some embodiments, STA may beperformed on samples that are enriched or depleted of certain sequencesand populations, e.g. by size selection, target capture, directeddegradation.

3. In some embodiments, it is possible to perform secondary multiplexPCRs or primer extension reactions to increase specificity and reduceundesirable products. For example, full nesting, semi-nesting,hemi-nesting, and/or subdividing into parallel reactions of smallerassay pools are all techniques that may be used to increase specificity.Experiments have shown that splitting a sample into three 400-plexreactions resulted in product DNA with greater specificity than one1,200-plex reaction with exactly the same primers. Similarly,experiments have shown that splitting a sample into four 2,400-plexreactions resulted in product DNA with greater specificity than one9,600-plex reaction with exactly the same primers. In an embodiment, itis possible to use target-specific and tag specific primers of the sameand opposing directionality.4. In some embodiments, it is possible to amplify a DNA sample(dilution, purified or otherwise) produced by an STA reaction usingtag-specific primers and “universal amplification”, i.e. to amplify manyor all pre-amplified and tagged targets. Primers may contain additionalfunctional sequences, e.g. barcodes, or a full adaptor sequencenecessary for sequencing on a high throughput sequencing platform.

These methods may be used for analysis of any sample of DNA, and areespecially useful when the sample of DNA is particularly small, or whenit is a sample of DNA where the DNA originates from more than oneindividual, such as in the case of maternal plasma. These methods may beused on DNA samples such as a single or small number of cells, genomicDNA, plasma DNA, amplified plasma libraries, amplified apoptoticsupernatant libraries, or other samples of mixed DNA. In an embodiment,these methods may be used in the case where cells of different geneticconstitution may be present in a single individual, such as with canceror transplants.

Protocol Variants (Variants and/or Additions to the Workflow Above)

Direct multiplexed mini-PCR: Specific target amplification (STA) of aplurality of target sequences with tagged primers is shown in FIG. 1.101 denotes double stranded DNA with a polymorphic locus of interest atX. 102 denotes the double stranded DNA with ligation adaptors added foruniversal amplification. 103 denotes the single stranded DNA that hasbeen universally amplified with PCR primers hybridized. 104 denotes thefinal PCR product. In some embodiments, STA may be done on more than100, more than 200, more than 500, more than 1,000, more than 2,000,more than 5,000, more than 10,000, more than 20,000, more than 50,000,more than 100,000 or more than 200,000 targets. In a subsequentreaction, tag-specific primers amplify all target sequences and lengthenthe tags to include all necessary sequences for sequencing, includingsample indexes. In an embodiment, primers may not be tagged or onlycertain primers may be tagged. Sequencing adaptors may be added byconventional adaptor ligation. In an embodiment, the initial primers maycarry the tags.

In an embodiment, primers are designed so that the length of DNAamplified is unexpectedly short. Prior art demonstrates that ordinarypeople skilled in the art typically design 100+ bp amplicons. In anembodiment, the amplicons may be designed to be less than 80 bp. In anembodiment, the amplicons may be designed to be less than 70 bp. In anembodiment, the amplicons may be designed to be less than 60 bp. In anembodiment, the amplicons may be designed to be less than 50 bp. In anembodiment, the amplicons may be designed to be less than 45 bp. In anembodiment, the amplicons may be designed to be less than 40 bp. In anembodiment, the amplicons may be designed to be less than 35 bp. In anembodiment, the amplicons may be designed to be between 40 and 65 bp.

An experiment was performed using this protocol using 1200-plexamplification. Both genomic DNA and pregnancy plasma were used; about70% of sequence reads mapped to targeted sequences. Details are givenelsewhere in this document. Sequencing of a 1042-plex without design andselection of assays resulted in >99% of sequences being primer dimerproducts.

Sequential PCR:

After STA1 multiple aliquots of the product may be amplified in parallelwith pools of reduced complexity with the same primers. The firstamplification can give enough material to split. This method isespecially good for small samples, for example those that are about6-100 pg, about 100 pg to 1 ng, about 1 ng to 10 ng, or about 10 ng to100 ng. The protocol was performed with 1200-plex into three 400-plexes.Mapping of sequencing reads increased from around 60 to 70% in the1200-plex alone to over 95%.

Semi-Nested Mini-PCR:

(see FIG. 2) After STA 1 a second STA is performed comprising amultiplex set of internal nested Forward primers (103 B, 105 b) and one(or few) tag-specific Reverse primers (103 A). 101 denotes doublestranded DNA with a polymorphic locus of interest at X. 102 denotes thedouble stranded DNA with ligation adaptors added for universalamplification. 103 denotes the single stranded DNA that has beenuniversally amplified with Forward primer B and Reverse Primer Ahybridized. 104 denotes the PCR product from 103. 105 denotes theproduct from 104 with nested Forward primer b hybridized, and Reversetag A already part of the molecule from the PCR that occurred between103 and 104. 106 denotes the final PCR product. With this workflowusually greater than 95% of sequences map to the intended targets. Thenested primer may overlap with the outer Forward primer sequence butintroduces additional 3′-end bases. In some embodiments it is possibleto use between one and 20 extra 3′ bases. Experiments have shown thatusing 9 or more extra 3′ bases in a 1200-plex designs works well.

Fully Nested Mini-PCR:

(see FIG. 3) After STA step 1, it is possible to perform a secondmultiplex PCR (or parallel m.p. PCRs of reduced complexity) with twonested primers carrying tags (A, a, B, b). 101 denotes double strandedDNA with a polymorphic locus of interest at X. 102 denotes the doublestranded DNA with ligation adaptors added for universal amplification.103 denotes the single stranded DNA that has been universally amplifiedwith Forward primer B and Reverse Primer A hybridized. 104 denotes thePCR product from 103. 105 denotes the product from 104 with nestedForward primer b and nested Reverse primer a hybridized. 106 denotes thefinal PCR product. In some embodiments, it is possible to use two fullsets of primers. Experiments using a fully nested mini-PCR protocol wereused to perform 146-plex amplification on single and three cells withoutstep 102 of appending universal ligation adaptors and amplifying.

Hemi-Nested Mini-PCR:

(see FIG. 4) It is possible to use target DNA that has and adaptors atthe fragment ends. STA is performed comprising a multiplex set ofForward primers (B) and one (or few) tag-specific Reverse primers (A). Asecond STA can be performed using a universal tag-specific Forwardprimer and target specific Reverse primer. 101 denotes double strandedDNA with a polymorphic locus of interest at X. 102 denotes the doublestranded DNA with ligation adaptors added for universal amplification.103 denotes the single stranded DNA that has been universally amplifiedwith Reverse Primer A hybridized. 104 denotes the PCR product from 103that was amplified using Reverse primer A and ligation adaptor tagprimer LT. 105 denotes the product from 104 with Forward primer Bhybridized. 106 denotes the final PCR product. In this workflow, targetspecific Forward and Reverse primers are used in separate reactions,thereby reducing the complexity of the reaction and preventing dimerformation of forward and reverse primers. Note that in this example,primers A and B may be considered to be first primers, and primers ‘a’and ‘b’ may be considered to be inner primers. This method is a bigimprovement on direct PCR as it is as good as direct PCR, but it avoidsprimer dimers. After first round of hemi nested protocol one typicallysees ˜99% non-targeted DNA, however, after second round there istypically a big improvement.

Triply Hemi-Nested Mini-PCR:

(see FIG. 5) It is possible to use target DNA that has and adaptor atthe fragment ends. STA is performed comprising a multiplex set ofForward primers (B) and one (or few) tag-specific Reverse primers (A)and (a). A second STA can be performed using a universal tag-specificForward primer and target specific Reverse primer. 101 denotes doublestranded DNA with a polymorphic locus of interest at X. 102 denotes thedouble stranded DNA with ligation adaptors added for universalamplification. 103 denotes the single stranded DNA that has beenuniversally amplified with Reverse Primer A hybridized. 104 denotes thePCR product from 103 that was amplified using Reverse primer A andligation adaptor tag primer LT. 105 denotes the product from 104 withForward primer B hybridized. 106 denotes the PCR product from 105 thatwas amplified using Reverse primer A and Forward primer B. 107 denotesthe product from 106 with Reverse primer ‘a’ hybridized. 108 denotes thefinal PCR product. Note that in this example, primers ‘a’ and B may beconsidered to be inner primers, and A may be considered to be a firstprimer. Optionally, both A and B may be considered to be first primers,and ‘a’ may be considered to be an inner primer. The designation ofreverse and forward primers may be switched. In this workflow, targetspecific Forward and Reverse primers are used in separate reactions,thereby reducing the complexity of the reaction and preventing dimerformation of forward and reverse primers. This method is a bigimprovement on direct PCR as it is as good as direct PCR, but it avoidsprimer dimers. After first round of hemi nested protocol one typicallysees ˜99% non-targeted DNA, however, after second round there istypically a big improvement.

One-Sided Nested Mini-PCR:

(see FIG. 6) It is possible to use target DNA that has an adaptor at thefragment ends. STA may also be performed with a multiplex set of nestedForward primers and using the ligation adapter tag as the Reverseprimer. A second STA may then be performed using a set of nested Forwardprimers and a universal Reverse primer. 101 denotes double stranded DNAwith a polymorphic locus of interest at X. 102 denotes the doublestranded DNA with ligation adaptors added for universal amplification.103 denotes the single stranded DNA that has been universally amplifiedwith Forward Primer A hybridized. 104 denotes the PCR product from 103that was amplified using Forward primer A and ligation adaptor tagReverse primer LT. 105 denotes the product from 104 with nested Forwardprimer a hybridized. 106 denotes the final PCR product. This method candetect shorter target sequences than standard PCR by using overlappingprimers in the first and second STAs. The method is typically performedoff a sample of DNA that has already undergone STA step 1above—appending of universal tags and amplification; the two nestedprimers are only on one side, other side uses the library tag. Themethod was performed on libraries of apoptotic supernatants andpregnancy plasma. With this workflow around 60% of sequences mapped tothe intended targets. Note that reads that contained the reverse adaptorsequence were not mapped, so this number is expected to be higher ifthose reads that contain the reverse adaptor sequence are mapped

One-Sided Mini-PCR:

It is possible to use target DNA that has an adaptor at the fragmentends (see FIG. 7). STA may be performed with a multiplex set of Forwardprimers and one (or few) tag-specific Reverse primer. 101 denotes doublestranded DNA with a polymorphic locus of interest at X. 102 denotes thedouble stranded DNA with ligation adaptors added for universalamplification. 103 denotes the single stranded DNA with Forward Primer Ahybridized. 104 denotes the PCR product from 103 that was amplifiedusing Forward Primer A and ligation adaptor tag Reverse primer LT, andwhich is the final PCR product. This method can detect shorter targetsequences than standard PCR. However, it may be relatively unspecific,as only one target specific primer is used. This protocol is effectivelyhalf of the one sided nested mini PCR

Reverse semi-nested mini-PCR: It is possible to use target DNA that hasan adaptor at the fragment ends (see FIG. 8). STA may be performed witha multiplex set of Forward primers and one (or few) tag-specific Reverseprimer. 101 denotes double stranded DNA with a polymorphic locus ofinterest at X. 102 denotes the double stranded DNA with ligationadaptors added for universal amplification. 103 denotes the singlestranded DNA with Reverse Primer B hybridized. 104 denotes the PCRproduct from 103 that was amplified using Reverse Primer B and ligationadaptor tag Forward primer LT. 105 denotes the PCR product 104 withhybridized Forward Primer A, and inner Reverse primer ‘b’. 106 denotesthe PCR product that has been amplified from 105 using Forward Primer Aand Reverse primer ‘b’, and which is the final PCR product. This methodcan detect shorter target sequences than standard PCR.

There also may be more variants that are simply iterations orcombinations of the above methods such as doubly nested PCR, where threesets of primers are used. Another variant is one-and-a-half sided nestedmini-PCR, where STA may also be performed with a multiplex set of nestedForward primers and one (or few) tag-specific Reverse primer.

Note that in all of these variants, the identity of the Forward primerand the Reverse primer may be interchanged. Note that in someembodiments, the nested variant can equally well be run without theinitial library preparation that comprises appending the adapter tags,and a universal amplification step. Note that in some embodiments,additional rounds of PCR may be included, with additional Forward and/orReverse primers and amplification steps; these additional steps may beparticularly useful if it is desirable to further increase the percentof DNA molecules that correspond to the targeted loci.

Nesting Workflows

There are many ways to perform the amplification, with different degreesof nesting, and with different degrees of multiplexing. In FIG. 9, aflow chart is given with some of the possible workflows. Note that theuse of 10,000-plex PCR is only meant to be an example; these flow chartswould work equally well for other degrees of multiplexing.

Looped Ligation Adaptors

When adding universal tagged adaptors for example for the purpose ofmaking a library for sequencing, there are a number of ways to ligateadaptors. One way is to blunt end the sample DNA, perform A-tailing, andligate with adaptors that have a T-overhang. There are a number of otherways to ligate adaptors. There are also a number of adaptors that can beligated. For example, a Y-adaptor can be used where the adaptor consistsof two strands of DNA where one strand has a double strand region, and aregion specified by a forward primer region, and where the other strandspecified by a double strand region that is complementary to the doublestrand region on the first strand, and a region with a reverse primer.The double stranded region, when annealed, may contain a T-overhang forthe purpose of ligating to double stranded DNA with an A overhang.

In an embodiment, the adaptor can be a loop of DNA where the terminalregions are complementary, and where the loop region contains a forwardprimer tagged region (LFT), a reverse primer tagged region (LRT), and acleavage site between the two (See FIG. 10). 101 refers to the doublestranded, blunt ended target DNA. 102 refers to the A-tailed target DNA.103 refers to the looped ligation adaptor with T overhang ‘T’ and thecleavage site ‘Z’. 104 refers to the target DNA with appended loopedligation adaptors. 105 refers to the target DNA with the ligationadaptors appended cleaved at the cleavage site. LFT refers to theligation adaptor Forward tag, and the LRT refers to the ligation adaptorReverse tag. The complementary region may end on a T overhang, or otherfeature that may be used for ligation to the target DNA. The cleavagesite may be a series of uracils for cleavage by UNG, or a sequence thatmay be recognized and cleaved by a restriction enzyme or other method ofcleavage or just a basic amplification. These adaptors can be uses forany library preparation, for example, for sequencing. These adaptors canbe used in combination with any of the other methods described herein,for example the mini-PCR amplification methods.

Internally Tagged Primers

When using sequencing to determine the allele present at a givenpolymorphic locus, the sequence read typically begins upstream of theprimer binding site (a), and then to the polymorphic site (X). Tags aretypically configured as shown in FIG. 11, left. 101 refers to the singlestranded target DNA with polymorphic locus of interest ‘X’, and primer‘a’ with appended tag ‘b’. In order to avoid nonspecific hybridization,the primer binding site (region of target DNA complementary to ‘a’) istypically 18 to 30 bp in length. Sequence tag ‘b’ is typically about 20bp; in theory these can be any length longer than about 15 bp, thoughmany people use the primer sequences that are sold by the sequencingplatform company. The distance ‘d’ between ‘a’ and ‘X’ may be at least 2bp so as to avoid allele bias. When performing multiplexed PCRamplification using the methods disclosed herein or other methods, wherecareful primer design is necessary to avoid excessive primer primerinteraction, the window of allowable distance ‘d’ between ‘a’ and ‘X’may vary quite a bit: from 2 bp to 10 bp, from 2 bp to 20 bp, from 2 bpto 30 bp, or even from 2 bp to more than 30 bp. Therefore, when usingthe primer configuration shown in FIG. 11, left, sequence reads must bea minimum of 40 bp to obtain reads long enough to measure thepolymorphic locus, and depending on the lengths of ‘a’ and ‘d’ thesequence reads may need to be up to 60 or 75 bp. Usually, the longer thesequence reads, the higher the cost and time of sequencing a givennumber of reads, therefore, minimizing the necessary read length cansave both time and money. In addition, since, on average, bases readearlier on the read are read more accurately than those read later onthe read, decreasing the necessary sequence read length can alsoincrease the accuracy of the measurements of the polymorphic region.

In an embodiment, termed internally tagged primers, the primer bindingsite (a) is split in to a plurality of segments (a′, a″, a′″, . . . ),and the sequence tag (b) is on a segment of DNA that is in the middle oftwo of the primer binding sites, as shown in FIG. 11, 103. Thisconfiguration allows the sequencer to make shorter sequence reads. In anembodiment, a′+a″ should be at least about 18 bp, and can be as long as30, 40, 50, 60, 80, 100 or more than 100 bp. In an embodiment, a″ shouldbe at least about 6 bp, and in an embodiment is between about 8 and 16bp. All other factors being equal, using the internally tagged primerscan cut the length of the sequence reads needed by at least 6 bp, asmuch as 8 bp, 10 bp, 12 bp, 15 bp, and even by as many as 20 or 30 bp.This can result in a significant money, time and accuracy advantage. Anexample of internally tagged primers is given in FIG. 12.

Primers with Ligation Adaptor Binding Region

One issue with fragmented DNA is that since it is short in length, thechance that a polymorphism is close to the end of a DNA strand is higherthan for a long strand (e.g. 101, FIG. 10). Since PCR capture of apolymorphism requires a primer binding site of suitable length on bothsides of the polymorphism, a significant number of strands of DNA withthe targeted polymorphism will be missed due to insufficient overlapbetween the primer and the targeted binding site. In an embodiment, thetarget DNA 101 can have ligation adaptors appended 102, and the targetprimer 103 can have a region (cr) that is complementary to the ligationadaptor tag (lt) appended upstream of the designed binding region (a)(see FIG. 13); thus in cases where the binding region (region of 101that is complementary to a) is shorter than the 18 bp typically requiredfor hybridization, the region (cr) on the primer than is complementaryto the library tag is able to increase the binding energy to a pointwhere the PCR can proceed. Note that any specificity that is lost due toa shorter binding region can be made up for by other PCR primers withsuitably long target binding regions. Note that this embodiment can beused in combination with direct PCR, or any of the other methodsdescribed herein, such as nested PCR, semi nested PCR, hemi nested PCR,one sided nested or semi or hemi nested PCR, or other PCR protocols.

When using the sequencing data to determine ploidy in combination withan analytical method that involves comparing the observed allele data tothe expected allele distributions for various hypotheses, eachadditional read from alleles with a low depth of read will yield moreinformation than a read from an allele with a high depth of read.Therefore, ideally, one would wish to see uniform depth of read (DOR)where each locus will have a similar number of representative sequencereads. Therefore, it is desirable to minimize the DOR variance. In anembodiment, it is possible to decrease the coefficient of variance ofthe DOR (this may be defined as the standard deviation of the DOR/theaverage DOR) by increasing the annealing times. In some embodiments theannealing temperatures may be longer than 2 minutes, longer than 4minutes, longer than ten minutes, longer than 30 minutes, and longerthan one hour, or even longer. Since annealing is an equilibriumprocess, there is no limit to the improvement of DOR variance withincreasing annealing times. In an embodiment, increasing the primerconcentration may decrease the DOR variance.

Diagnostic Box

In an embodiment, the present disclosure comprises a diagnostic box thatis capable of partly or completely carrying out any of the methodsdescribed in this disclosure. In an embodiment, the diagnostic box maybe located at a physician's office, a hospital laboratory, or anysuitable location reasonably proximal to the point of patient care. Thebox may be able to run the entire method in a wholly automated fashion,or the box may require one or a number of steps to be completed manuallyby a technician. In an embodiment, the box may be able to analyze atleast the genotypic data measured on the maternal plasma. In anembodiment, the box may be linked to means to transmit the genotypicdata measured on the diagnostic box to an external computation facilitywhich may then analyze the genotypic data, and possibly also generate areport. The diagnostic box may include a robotic unit that is capable oftransferring aqueous or liquid samples from one container to another. Itmay comprise a number of reagents, both solid and liquid. It maycomprise a high throughput sequencer. It may comprise a computer.

Primer Kit

In some embodiments, a kit may be formulated that comprises a pluralityof primers designed to achieve the methods described in this disclosure.The primers may be outer forward and reverse primers, inner forward andreverse primers as disclosed herein, they could be primers that havebeen designed to have low binding affinity to other primers in the kitas disclosed in the section on primer design, they could be hybridcapture probes or pre-circularized probes as described in the relevantsections, or some combination thereof. In an embodiment, a kit may beformulated for determining a ploidy status of a target chromosome in agestating fetus designed to be used with the methods disclosed herein,the kit comprising a plurality of inner forward primers and optionallythe plurality of inner reverse primers, and optionally outer forwardprimers and outer reverse primers, where each of the primers is designedto hybridize to the region of DNA immediately upstream and/or downstreamfrom one of the polymorphic sites on the target chromosome, andoptionally additional chromosomes. In an embodiment, the primer kit maybe used in combination with the diagnostic box described elsewhere inthis document.

Compositions of DNA

When performing an informatics analysis on sequencing data measured on amixture of fetal and maternal blood to determine genomic informationpertaining to the fetus, for example the ploidy state of the fetus, itmay be advantageous to measure the allele distributions at a set ofalleles. Unfortunately, in many cases, such as when attempting todetermine the ploidy state of a fetus from the DNA mixture found in theplasma of a maternal blood sample, the amount of DNA available is notsufficient to directly measure the allele distributions with goodfidelity in the mixture. In these cases, amplification of the DNAmixture will provide sufficient numbers of DNA molecules that thedesired allele distributions may be measured with good fidelity.However, current methods of amplification typically used in theamplification of DNA for sequencing are often very biased, meaning thatthey do not amplify both alleles at a polymorphic locus by the sameamount. A biased amplification can result in allele distributions thatare quite different from the allele distributions in the originalmixture. For most purposes, highly accurate measurements of the relativeamounts of alleles present at polymorphic loci are not needed. Incontrast, in an embodiment of the present disclosure, amplification orenrichment methods that specifically enrich polymorphic alleles andpreserve allelic ratios is advantageous.

A number of methods are described herein that may be used topreferentially enrich a sample of DNA at a plurality of loci in a waythat minimizes allelic bias. Some examples are using circularizingprobes to target a plurality of loci where the 3′ ends and 5′ ends ofthe pre-circularized probe are designed to hybridize to bases that areone or a few positions away from the polymorphic sites of the targetedallele. Another is to use PCR probes where the 3′ end PCR probe isdesigned to hybridize to bases that are one or a few positions away fromthe polymorphic sites of the targeted allele. Another is to use a splitand pool approach to create mixtures of DNA where the preferentiallyenriched loci are enriched with low allelic bias without the drawbacksof direct multiplexing. Another is to use a hybrid capture approachwhere the capture probes are designed such that the region of thecapture probe that is designed to hybridize to the DNA flanking thepolymorphic site of the target is separated from the polymorphic site byone or a small number of bases.

In the case where measured allele distributions at a set of polymorphicloci are used to determine the ploidy state of an individual, it isdesirable to preserve the relative amounts of alleles in a sample of DNAas it is prepared for genetic measurements. This preparation may involveWGA amplification, targeted amplification, selective enrichmenttechniques, hybrid capture techniques, circularizing probes or othermethods meant to amplify the amount of DNA and/or selectively enhancethe presence of molecules of DNA that correspond to certain alleles.

In some embodiments of the present disclosure, there is a set of DNAprobes designed to target loci where the loci have maximal minor allelefrequencies. In some embodiments of the present disclosure, there is aset of probes that are designed to target where the loci have themaximum likelihood of the fetus having a highly informative SNP at thoseloci. In some embodiments of the present disclosure, there is a set ofprobes that are designed to target loci where the probes are optimizedfor a given population subgroup. In some embodiments of the presentdisclosure, there is a set of probes that are designed to target lociwhere the probes are optimized for a given mix of population subgroups.In some embodiments of the present disclosure, there is a set of probesthat are designed to target loci where the probes are optimized for agiven pair of parents which are from different population subgroups thathave different minor allele frequency profiles. In some embodiments ofthe present disclosure, there is a circularized strand of DNA thatcomprises at least one base pair that annealed to a piece of DNA that isof fetal origin. In some embodiments of the present disclosure, there isa circularized strand of DNA that comprises at least one base pair thatannealed to a piece of DNA that is of placental origin. In someembodiments of the present disclosure, there is a circularized strand ofDNA that circularized while at least some of the nucleotides wereannealed to DNA that was of fetal origin. In some embodiments of thepresent disclosure, there is a circularized strand of DNA thatcircularized while at least some of the nucleotides were annealed to DNAthat was of placental origin. In some embodiments of the presentdisclosure, there is a set of probes wherein some of the probes targetsingle tandem repeats, and some of the probes target single nucleotidepolymorphisms. In some embodiments, the loci are selected for thepurpose of non-invasive prenatal diagnosis. In some embodiments, theprobes are used for the purpose of non-invasive prenatal diagnosis. Insome embodiments, the loci are targeted using a method that couldinclude circularizing probes, MIPs, capture by hybridization probes,probes on a SNP array, or combinations thereof. In some embodiments, theprobes are used as circularizing probes, MIPs, capture by hybridizationprobes, probes on a SNP array, or combinations thereof. In someembodiments, the loci are sequenced for the purpose of non-invasiveprenatal diagnosis.

In the case where the relative informativeness of a sequence is greaterwhen combined with relevant parent contexts, it follows that maximizingthe number of sequence reads that contain a SNP for which the parentalcontext is known may maximize the informativeness of the set ofsequencing reads on the mixed sample. In an embodiment, the number ofsequence reads that contain a SNP for which the parent contexts areknown may be enhanced by using qPCR to preferentially amplify specificsequences. In an embodiment, the number of sequence reads that contain aSNP for which the parent contexts are known may be enhanced by usingcircularizing probes (for example, MIPs) to preferentially amplifyspecific sequences. In an embodiment, the number of sequence reads thatcontain a SNP for which the parent contexts are known may be enhanced byusing a capture by hybridization method (for example SURESELECT) topreferentially amplify specific sequences. Different methods may be usedto enhance the number of sequence reads that contain a SNP for which theparent contexts are known. In an embodiment, the targeting may beaccomplished by extension ligation, ligation without extension, captureby hybridization, or PCR.

In a sample of fragmented genomic DNA, a fraction of the DNA sequencesmap uniquely to individual chromosomes; other DNA sequences may be foundon different chromosomes. Note that DNA found in plasma, whethermaternal or fetal in origin is typically fragmented, often at lengthsunder 500 bp. In a typical genomic sample, roughly 3.3% of the mappablesequences will map to chromosome 13; 2.2% of the mappable sequences willmap to chromosome 18; 1.35% of the mappable sequences will map tochromosome 21; 4.5% of the mappable sequences will map to chromosome Xin a female; 2.25% of the mappable sequences will map to chromosome X(in a male); and 0.73% of the mappable sequences will map to chromosomeY (in a male). These are the chromosomes that are most likely to beaneuploid in a fetus. Also, among short sequences, approximately 1 in 20sequences will contain a SNP, using the SNPs contained on dbSNP. Theproportion may well be higher given that there may be many SNPs thathave not been discovered.

In an embodiment of the present disclosure, targeting methods may beused to enhance the fraction of DNA in a sample of DNA that map to agiven chromosome such that the fraction significantly exceeds thepercentages listed above that are typical for genomic samples. In anembodiment of the present disclosure, targeting methods may be used toenhance the fraction of DNA in a sample of DNA such that the percentageof sequences that contain a SNP are significantly greater than what maybe found in typical for genomic samples. In an embodiment of the presentdisclosure, targeting methods may be used to target DNA from achromosome or from a set of SNPs in a mixture of maternal and fetal DNAfor the purposes of prenatal diagnosis.

Note that a method has been reported (U.S. Pat. No. 7,888,017) fordetermining fetal aneuploidy by counting the number of reads that map toa suspect chromosome and comparing it to the number of reads that map toa reference chromosome, and using the assumption that an overabundanceof reads on the suspect chromosome corresponds to a triploidy in thefetus at that chromosome. Those methods for prenatal diagnosis would notmake use of targeting of any sort, nor do they describe the use oftargeting for prenatal diagnosis.

By making use of targeting approaches in sequencing the mixed sample, itmay be possible to achieve a certain level of accuracy with fewersequence reads. The accuracy may refer to sensitivity, it may refer tospecificity, or it may refer to some combination thereof. The desiredlevel of accuracy may be between 90% and 95%; it may be between 95% and98%; it may be between 98% and 99%; it may be between 99% and 99.5%; itmay be between 99.5% and 99.9%; it may be between 99.9% and 99.99%; itmay be between 99.99% and 99.999%, it may be between 99.999% and 100%.Levels of accuracy above 95% may be referred to as high accuracy.

There are a number of published methods in the prior art thatdemonstrate how one may determine the ploidy state of a fetus from amixed sample of maternal and fetal DNA, for example: G. J. W. Liao etal. Clinical Chemistry 2011; 57(1) pp. 92-101. These methods focus onthousands of locations along each chromosome. The number of locationsalong a chromosome that may be targeted while still resulting in a highaccuracy ploidy determination on a fetus, for a given number of sequencereads, from a mixed sample of DNA is unexpectedly low. In an embodimentof the present disclosure, an accurate ploidy determination may be madeby using targeted sequencing, using any method of targeting, for exampleqPCR, ligand mediated PCR, other PCR methods, capture by hybridization,or circularizing probes, wherein the number of loci along a chromosomethat need to be targeted may be between 5,000 and 2,000 loci; it may bebetween 2,000 and 1,000 loci; it may be between 1,000 and 500 loci; itmay be between 500 and 300 loci; it may be between 300 and 200 loci; itmay be between 200 and 150 loci; it may be between 150 and 100 loci; itmay be between 100 and 50 loci; it may be between 50 and 20 loci; it maybe between 20 and 10 loci. Optimally, it may be between 100 and 500loci. The high level of accuracy may be achieved by targeting a smallnumber of loci and executing an unexpectedly small number of sequencereads. The number of reads may be between 100 million and 50 millionreads; the number of reads may be between 50 million and 20 millionreads; the number of reads may be between 20 million and 10 millionreads; the number of reads may be between 10 million and 5 millionreads; the number of reads may be between 5 million and 2 million reads;the number of reads may be between 2 million and 1 million; the numberof reads may be between 1 million and 500,000; the number of reads maybe between 500,000 and 200,000; the number of reads may be between200,000 and 100,000; the number of reads may be between 100,000 and50,000; the number of reads may be between 50,000 and 20,000; the numberof reads may be between 20,000 and 10,000; the number of reads may bebelow 10,000. Fewer number of read are necessary for larger amounts ofinput DNA.

In some embodiments, there is a composition comprising a mixture of DNAof fetal origin, and DNA of maternal origin, wherein the percent ofsequences that uniquely map to chromosome 13 is greater than 4%, greaterthan 5%, greater than 6%, greater than 7%, greater than 8%, greater than9%, greater than 10%, greater than 12%, greater than 15%, greater than20%, greater than 25%, or greater than 30%. In some embodiments of thepresent disclosure, there is a composition comprising a mixture of DNAof fetal origin, and DNA of maternal origin, wherein the percent ofsequences that uniquely map to chromosome 18 is greater than 3%, greaterthan 4%, greater than 5%, greater than 6%, greater than 7%, greater than8%, greater than 9%, greater than 10%, greater than 12%, greater than15%, greater than 20%, greater than 25%, or greater than 30%. In someembodiments of the present disclosure, there is a composition comprisinga mixture of DNA of fetal origin, and DNA of maternal origin, whereinthe percent of sequences that uniquely map to chromosome 21 is greaterthan 2%, greater than 3%, greater than 4%, greater than 5%, greater than6%, greater than 7%, greater than 8%, greater than 9%, greater than 10%,greater than 12%, greater than 15%, greater than 20%, greater than 25%,or greater than 30%. In some embodiments of the present disclosure,there is a composition comprising a mixture of DNA of fetal origin, andDNA of maternal origin, wherein the percent of sequences that uniquelymap to chromosome X is greater than 6%, greater than 7%, greater than8%, greater than 9%, greater than 10%, greater than 12%, greater than15%, greater than 20%, greater than 25%, or greater than 30%. In someembodiments of the present disclosure, there is a composition comprisinga mixture of DNA of fetal origin, and DNA of maternal origin, whereinthe percent of sequences that uniquely map to chromosome Y is greaterthan 1%, greater than 2%, greater than 3%, greater than 4%, greater than5%, greater than 6%, greater than 7%, greater than 8%, greater than 9%,greater than 10%, greater than 12%, greater than 15%, greater than 20%,greater than 25%, or greater than 30%.

In some embodiments, a composition is described comprising a mixture ofDNA of fetal origin, and DNA of maternal origin, wherein the percent ofsequences that uniquely map to a chromosome, and that contains at leastone single nucleotide polymorphism is greater than 0.2%, greater than0.3%, greater than 0.4%, greater than 0.5%, greater than 0.6%, greaterthan 0.7%, greater than 0.8%, greater than 0.9%, greater than 1%,greater than 1.2%, greater than 1.4%, greater than 1.6%, greater than1.8%, greater than 2%, greater than 2.5%, greater than 3%, greater than4%, greater than 5%, greater than 6%, greater than 7%, greater than 8%,greater than 9%, greater than 10%, greater than 12%, greater than 15%,or greater than 20%, and where the chromosome is taken from the group13, 18, 21, X, or Y. In some embodiments of the present disclosure,there is a composition comprising a mixture of DNA of fetal origin, andDNA of maternal origin, wherein the percent of sequences that uniquelymap to a chromosome and that contain at least one single nucleotidepolymorphism from a set of single nucleotide polymorphisms is greaterthan 0.15%, greater than 0.2%, greater than 0.3%, greater than 0.4%,greater than 0.5%, greater than 0.6%, greater than 0.7%, greater than0.8%, greater than 0.9%, greater than 1%, greater than 1.2%, greaterthan 1.4%, greater than 1.6%, greater than 1.8%, greater than 2%,greater than 2.5%, greater than 3%, greater than 4%, greater than 5%,greater than 6%, greater than 7%, greater than 8%, greater than 9%,greater than 10%, greater than 12%, greater than 15%, or greater than20%, where the chromosome is taken from the set of chromosome 13, 18,21, X and Y, and where the number of single nucleotide polymorphisms inthe set of single nucleotide polymorphisms is between 1 and 10, between10 and 20, between 20 and 50, between 50 and 100, between 100 and 200,between 200 and 500, between 500 and 1,000, between 1,000 and 2,000,between 2,000 and 5,000, between 5,000 and 10,000, between 10,000 and20,000, between 20,000 and 50,000, and between 50,000 and 100,000.

In theory, each cycle in the amplification doubles the amount of DNApresent; however, in reality, the degree of amplification is slightlylower than two. In theory, amplification, including targetedamplification, will result in bias free amplification of a DNA mixture;in reality, however, different alleles tend to be amplified to adifferent extent than other alleles. When DNA is amplified, the degreeof allelic bias typically increases with the number of amplificationsteps. In some embodiments, the methods described herein involveamplifying DNA with a low level of allelic bias. Since the allelic biascompounds with each additional cycle, one can determine the per cycleallelic bias by calculating the nth root of the overall bias where n isthe base 2 logarithm of degree of enrichment. In some embodiments, thereis a composition comprising a second mixture of DNA, where the secondmixture of DNA has been preferentially enriched at a plurality ofpolymorphic loci from a first mixture of DNA where the degree ofenrichment is at least 10, at least 100, at least 1,000, at least10,000, at least 100,000 or at least 1,000,000, and where the ratio ofthe alleles in the second mixture of DNA at each locus differs from theratio of the alleles at that locus in the first mixture of DNA by afactor that is, on average, less than 1,000%, 500%, 200%, 100%, 50%,20%, 10%, 5%, 2%, 1%, 0.5%, 0.2%, 0.1%, 0.05%, 0.02%, or 0.01%. In someembodiments, there is a composition comprising a second mixture of DNA,where the second mixture of DNA has been preferentially enriched at aplurality of polymorphic loci from a first mixture of DNA where the percycle allelic bias for the plurality of polymorphic loci is, on average,less than 10%, 5%, 2%, 1%, 0.5%, 0.2%, 0.1%, 0.05%, or 0.02%. In someembodiments, the plurality of polymorphic loci comprises at least 10loci, at least 20 loci, at least 50 loci, at least 100 loci, at least200 loci, at least 500 loci, at least 1,000 loci, at least 2,000 loci,at least 5,000 loci, at least 10,000 loci, at least 20,000 loci, or atleast 50,000 loci.

Maximum Likelihood Estimates

Most methods known in the art for detecting the presence or absence ofbiological phenomenon or medical condition involve the use of a singlehypothesis rejection test, where a metric that is correlated with thecondition is measured, and if the metric is on one side of a giventhreshold, the condition is present, while of the metric falls on theother side of the threshold, the condition is absent. Asingle-hypothesis rejection test only looks at the null distributionwhen deciding between the null and alternate hypotheses. Without takinginto account the alternate distribution, one cannot estimate thelikelihood of each hypothesis given the observed data and thereforecannot calculate a confidence on the call. Hence with asingle-hypothesis rejection test, one gets a yes or no answer without afeeling for the confidence associated with the specific case.

In some embodiments, the method disclosed herein is able to detect thepresence or absence of biological phenomenon or medical condition usinga maximum likelihood method. This is a substantial improvement over amethod using a single hypothesis rejection technique as the thresholdfor calling absence or presence of the condition can be adjusted asappropriate for each case. This is particularly relevant for diagnostictechniques that aim to determine the presence or absence of aneuploidyin a gestating fetus from genetic data available from the mixture offetal and maternal DNA present in the free floating DNA found inmaternal plasma. This is because as the fraction of fetal DNA in theplasma derived fraction changes, the optimal threshold for callinganeuploidy vs. euploidy changes. As the fetal fraction drops, thedistribution of data that is associated with an aneuploidy becomesincreasingly similar to the distribution of data that is associated witha euploidy.

The maximum likelihood estimation method uses the distributionsassociated with each hypothesis to estimate the likelihood of the dataconditioned on each hypothesis. These conditional probabilities can thenbe converted to a hypothesis call and confidence. Similarly, maximum aposteriori estimation method uses the same conditional probabilities asthe maximum likelihood estimate, but also incorporates population priorswhen choosing the best hypothesis and determining confidence.

Therefore, the use of a maximum likelihood estimate (MLE) technique, orthe closely related maximum a posteriori (MAP) technique give twoadvantages, first it increases the chance of a correct call, and it alsoallows a confidence to be calculated for each call. In an embodiment,selecting the ploidy state corresponding to the hypothesis with thegreatest probability is carried out using maximum likelihood estimatesor maximum a posteriori estimates. In an embodiment, a method isdisclosed for determining the ploidy state of a gestating fetus thatinvolves taking any method currently known in the art that uses a singlehypothesis rejection technique and reformulating it such that it uses aMLE or MAP technique. Some examples of methods that can be significantlyimproved by applying these techniques can be found in U.S. Pat. Nos.8,008,018, 7,888,017, or U.S. Pat. No. 7,332,277.

In an embodiment, a method is described for determining presence orabsence of fetal aneuploidy in a maternal plasma sample comprising fetaland maternal genomic DNA, the method comprising: obtaining a maternalplasma sample; measuring the DNA fragments found in the plasma samplewith a high throughput sequencer; mapping the sequences to thechromosome and determining the number of sequence reads that map to eachchromosome; calculating the fraction of fetal DNA in the plasma sample;calculating an expected distribution of the amount of a targetchromosome that would be expected to be present if that if the secondtarget chromosome were euploid and one or a plurality of expecteddistributions that would be expected if that chromosome were aneuploid,using the fetal fraction and the number of sequence reads that map toone or a plurality of reference chromosomes expected to be euploid; andusing a MLE or MAP determine which of the distributions is most likelyto be correct, thereby indicating the presence or absence of a fetalaneuploidy. In an embodiment, the measuring the DNA from the plasma mayinvolve conducting massively parallel shotgun sequencing. In anembodiment, the measuring the DNA from the plasma sample may involvesequencing DNA that has been preferentially enriched, for examplethrough targeted amplification, at a plurality of polymorphic ornon-polymorphic loci. The plurality of loci may be designed to targetone or a small number of suspected aneuploid chromosomes and one or asmall number of reference chromosomes. The purpose of the preferentialenrichment is to increase the number of sequence reads that areinformative for the ploidy determination.

Ploidy Calling Informatics Methods

Described herein is a method for determining the ploidy state of a fetusgiven sequence data. In some embodiments, this sequence data may bemeasured on a high throughput sequencer. In some embodiments, thesequence data may be measured on DNA that originated from free floatingDNA isolated from maternal blood, wherein the free floating DNAcomprises some DNA of maternal origin, and some DNA of fetal/placentalorigin. This section will describe one embodiment of the presentdisclosure in which the ploidy state of the fetus is determined assumingthat fraction of fetal DNA in the mixture that has been analyzed is notknown and will be estimated from the data. It will also describe anembodiment in which the fraction of fetal DNA (“fetal fraction”) or thepercentage of fetal DNA in the mixture can be measured by anothermethod, and is assumed to be known in determining the ploidy state ofthe fetus. In some embodiments the fetal fraction can be calculatedusing only the genotyping measurements made on the maternal blood sampleitself, which is a mixture of fetal and maternal DNA. In someembodiments the fraction may be calculated also using the measured orotherwise known genotype of the mother and/or the measured or otherwiseknown genotype of the father. In another embodiment ploidy state of thefetus can be determined solely based on the calculated fraction of fetalDNA for the chromosome in question compared to the calculated fractionof fetal DNA for the reference chromosome assumed disomic.

In the preferred embodiment, suppose that, for a particular chromosome,we observe and analyze N SNPs, for which we have:

-   -   Set of NR free floating DNA sequence measurements S=(s₁, . . . ,        s_(NR)). Since this method utilizes the SNP measurements, all        sequence data that corresponds to non-polymorphic loci can be        disregarded. In a simplified version, where we have (A,B) counts        on each SNP, where A and B correspond to the two alleles present        at a given locus, S can be written as S=((a₁,b₁), . . . ,        (a_(N), b_(N))), where a, is the A count on SNP i, b_(i) is the        B count on SNP i, and Σ_(i=1:N)(a_(i)+b₁)=NR    -   Parent data consisting of        -   genotypes from a SNP microarray or other intensity based            genotyping platform: mother M=(m₁, . . . , m_(N)), father            F=(f₁, . . . , f_(N)), where m_(i), f_(i)∈(AA,AB, BB).        -   AND/OR sequence data measurements: NRM mother measurements            SM=(sm₁, . . . , sm_(arm)), NRF father measurements SF=(sf₁,            . . . , sf_(nrf)). Similar to the above simplification, if            we have (A,B) counts on each SNP SM=((am₁,bm₁), . . .            (am_(N), bm_(N))), SF=((af₁,bf₁), . . . (af_(N), bf_(N)))

Collectively, the mother, father child data are denoted asD=(M,F,SM,SF,S). Note that the parent data is desired and increases theaccuracy of the algorithm, but is NOT necessary, especially the fatherdata. This means that even in the absence of mother and/or father data,it is possible to get very accurate copy number results.

It is possible to derive the best copy number estimate (H*) bymaximizing the data log likelihood LIK(D|H) over all hypotheses (H)considered. In particular, it is possible to determine the relativeprobability of each of the ploidy hypotheses using the jointdistribution model and the allele counts measured on the preparedsample, and using those relative probabilities to determine thehypothesis most likely to be correct as follows:

$H^{*} = {\underset{H}{\arg \; \max}\; {{LIK}\left( D \middle| H \right)}}$

Similarly the posteriori hypothesis likelihood given the data may bewritten as:

$H^{*} = {\underset{H}{\arg \; \max}\mspace{14mu} {{LIK}\left( D \middle| H \right)}*{{priorprob}(H)}}$

Where priorprob(H) is the prior probability assigned to each hypothesisH, based on model design and prior knowledge.It is also possible to use priors to find the maximum a posterioriestimate:

$H_{MA} = {\underset{H}{{\arg \; \max}\mspace{11mu}}{{LIK}\left( D \middle| H \right)}}$

In an embodiment, the copy number hypotheses that may be considered are:

-   -   Monosomy:        -   maternal H10 (one copy from mother)        -   paternal H01 (one copy from father)    -   Disomy: H11 (one copy each mother and father)    -   Simple trisomy, no crossovers considered:        -   Maternal: H21_matched (two identical copies from mother, one            copy from father), H21_unmatched (BOTH copies from mother,            one copy from father)        -   Paternal: H12_matched (one copy from mother, two identical            copies from father), H12_unmatched (one copy from mother,            both copies from father)    -   Composite trisomy, allowing for crossovers (using a joint        distribution model):        -   maternal H21 (two copies from mother, one from father),        -   paternal H12 (one copy from mother, two copies from father)

In other embodiments, other ploidy states, such as nullsomy (H00),uniparental disomy (H20 and H02), and tetrasomy (H04, H13, H22, H31 andH40), may be considered.

If there are no crossovers, each trisomy, whether the origin wasmitotis, meiosis I, or meiosis II, would be one of the matched orunmatched trisomies. Due to crossovers, true trisomy is usually acombination of the two. First, a method to derive hypothesis likelihoodsfor simple hypotheses is described. Then a method to derive hypothesislikelihoods for composite hypotheses is described, combining individualSNP likelihood with crossovers.

LIK(D|H) for a Simple Hypothesis

In an embodiment, LIK(D|H) may be determined for simple hypotheses, asfollows. For simple hypotheses H, LIK(H), the log likelihood ofhypothesis H on a whole chromosome, may be calculated as the sum of loglikelihoods of individual SNPs, assuming known or derived child fractioncf. In an embodiment it is possible to derive cf from the data.

${{LIK}\left( D \middle| H \right)} = {\sum\limits_{i}{{LIK}\left( {\left. D \middle| H \right.,{cf},i} \right)}}$

This hypothesis does not assume any linkage between SNPs, and thereforedoes not utilize a joint distribution model.

In some embodiments, the Log Likelihood may be determined on a per SNPbasis. On a particular SNP i, assuming fetal ploidy hypothesis H andpercent fetal DNA cf, log likelihood of observed data D is defined as:

$\begin{matrix}{{{LIK}\left( {\left. D \middle| H \right.,i} \right)} = {\log \mspace{14mu} {P\left( {\left. D \middle| H \right.,{cf},i} \right)}}} \\{= {\log\left( {\sum\limits_{m,f,c}{{P\left( {\left. D \middle| m \right.,f,c,H,{cf},i} \right)}{P\left( {\left. c \middle| m \right.,f,H} \right)}{P\left( m \middle| i \right)}{P\left( f \middle| i \right)}}} \right)}}\end{matrix}\quad$

where m are possible true mother genotypes, f are possible true fathergenotypes, where m,f∈{AA,AB,BB}, and c are possible child genotypesgiven the hypothesis H. In particular, for monosomy c∈{A, B}, for disomyc∈{AA, AB, BB}, for trisomy c∈{AAA, AAB, ABB, BBB}.

Genotype prior frequency: p(m|i) is the general prior probability ofmother genotype m on SNP i, based on the known population frequency atSNP I, denoted pA_(i). In particular

p(AA|pA _(i))=(pA _(i))² ,p(AB|pA _(i))=2(pA _(i))*(1−pA _(i)),p(BB|pA_(i))=(1−pA _(i))²

Father genotype probability, p(f|i), may be determined in an analogousfashion.

True child probability: p(c|m, f, H) is the probability of getting truechild genotype=c, given parents m, f, and assuming hypothesis H, whichcan be easily calculated. For example, for H11, H21 matched and H21unmatched, p(c|m,f,H) is given below.

p(c|m, f, H) H11 H21 matched H21 unmatched m f AA AB BB AAA AAB ABB BBBAAA AAB ABB BBB AA AA 1 0 0 1 0 0 0 1 0 0 0 AB AA 0.5 0.5 0 0.5 0 0.5 00 1 0 0 BB AA 0 1 0 0 0 1 0 0 0 1 0 AA AB 0.5 0.5 0 0.5 0.5 0 0 0.5 0.50 0 AB AB 0.25 0.5 0.25 0.25 0.25 0.25 0.25 0 0.5 0.5 0 BB AB 0 0.5 0.50 0 0.5 0.5 0 0 0.5 0.5 AA BB 0 1 0 0 1 0 0 0 1 0 0 AB BB 0 0.5 0.5 00.5 0 0.5 0 0 1 0 BB BB 0 0 1 0 0 0 1 0 0 0 1

Data likelihood: P(D|m, f, c, H, i, cf) is the probability of given dataD on SNP i, given true mother genotype m, true father genotype f, truechild genotype c, hypothesis H and child fraction cf. It can be brokendown into the probability of mother, father and child data as follows:

P(D|m,f,c,H,cf,=P(SM|m,i)P(M|m,i)P(SF|f,i)P(F|f,i)P(S|m,c,H,cf,i)

Mother SNP array data likelihood: Probability of mother SNP arraygenotype data m_(i) at SNP i compared to true genotype m, assuming SNParray genotypes are correct, is simply

${P\left( {\left. M \middle| m \right.,i} \right)} = \left\{ \begin{matrix}1 & {m_{i} = m} \\0 & {m_{i} \neq m}\end{matrix} \right.$

Mother sequence data likelihood: the probability of the mother sequencedata at SNP i, in the case of counts S_(i)=(am_(i),bm_(i)), with noextra noise or bias involved, is the binomial probability defined asP(SM|m,i)=P_(X|m)(am_(i)) where X|m˜Binom(p_(m)(A), am_(i)+bm_(i)) withp_(m)(A) defined as

m AA AB BB A B nocall p(A) 1 0.5 0 1 0 0.5

Father data likelihood: a similar equation applies for father datalikelihood.

Note that it is possible to determine the child genotype without theparent data, especially father data. For example if no father genotypedata F is available, one may just use P(F|f, i)=1. If no father sequencedata SF is available, one may just use P(SF|f,i)=1.

In some embodiments, the method involves building a joint distributionmodel for the expected allele counts at a plurality of polymorphic locion the chromosome for each ploidy hypothesis; one method to accomplishsuch an end is described here. Free fetal DNA data likelihood: P(S|m, c,H, cf, i) is the probability of free fetal DNA sequence data on SNP i,given true mother genotype m, true child genotype c, child copy numberhypothesis H, and assuming child fraction cf. It is in fact theprobability of sequence data S on SNP I, given the true probability of Acontent on SNP i μ(m, c, cf, H)

P(S|m,c,H,cf,=P(S|μ(m,c,cf,H),i)

For counts, where S_(i)(a_(i),b_(i)), with no extra noise or bias indata involved,

P(S|μ(m,c,cf,H),i)=P _(x)(a _(i))

where X˜Binom(p(A), a_(i)+b_(i)) with p(A)=μ(m, c, cf, H). In a morecomplex case where the exact alignment and (A,B) counts per SNP are notknown, P(S|μ(m, c, cf, H), i) is a combination of integrated binomials.

True A content probability: μ(m, c, cf, H), the true probability of Acontent on SNP i in this mother/child mixture, assuming that true mothergenotype=m, true child genotype=c, and overall child fraction=cf, isdefined as

${\mu \left( {m,c,{cf},H} \right)} = \frac{{\# \mspace{11mu} {A(m)}*\left( {1 - {cf}} \right)} + {\# \; {A(c)}*{cf}}}{{n_{m}*\left( {1 - {cf}} \right)} + {n_{c}*{cf}}}$

where #A(g)=number of A's in genotype g, n_(m)=2 is somy of mother andn_(c) is ploidy of the child under hypothesis H (1 for monosomy, 2 fordisomy, 3 for trisomy).

Using a Joint Distribution Model: LIK(D|H) for a Composite Hypothesis

In some embodiments, the method involves building a joint distributionmodel for the expected allele counts at the plurality of polymorphicloci on the chromosome for each ploidy hypothesis; one method toaccomplish such an end is described here. In many cases, trisomy isusually not purely matched or unmatched, due to crossovers, so in thissection results for composite hypotheses H21 (maternal trisomy) and H12(paternal trisomy) are derived, which combine matched and unmatchedtrisomy, accounting for possible crossovers.

In the case of trisomy, if there were no crossovers, trisomy would besimply matched or unmatched trisomy. Matched trisomy is where childinherits two copies of the identical chromosome segment from one parent.Unmatched trisomy is where child inherits one copy of each homologouschromosome segment from the parent. Due to crossovers, some segments ofa chromosome may have matched trisomy, and other parts may haveunmatched trisomy. Described in this section is how to build a jointdistribution model for the heterozygosity rates for a set of alleles;that is, for the expected allele counts at a number of loci for one ormore hypotheses.

Suppose that on SNP i, LIK(D|Hm, i) is the fit for matched hypothesisH_(m), and LIK(D|Hu, i) is the fit for unmatched hypothesis H_(u), andpc(i)=probability of crossover between SNPs i−1 and i. One may thencalculate the full likelihood as:

LIK(D|H)=Σ_(E) LIK(D|E,1:N)

where LIK(D|E, 1:N) is the likelihood of ending in hypothesis E, forSNPs 1:N. E=hypothesis of the last SNP, E∈(Hm, Hu). Recursively, one maycalculate:

LIK(D|E,1:i)=LIK(D|E,i)+log(exp(LIK(D|E,1:i−1))*(1−pc(i))+exp(LIK(D|˜E,1:i−1))*pc(i))

where ˜E is the hypothesis other than E (not E), where hypothesesconsidered are H_(m) and H_(u). In particular, one may calculate thelikelihood of 1:i SNPs, based on likelihood of 1 to (i−1) SNPs witheither the same hypothesis and no crossover, or the opposite hypothesisand a crossover, multiplied by the likelihood of the SNP i

For SNP 1,i=1,LIK(D|E,1:1)=LIK(D|E,1).

For SNP2,i=2,LIK(D|E,1:2)=LIK(D|E,2)+log(exp(LIK(D|E,1))*(1−pc(2))+exp(LIK(D|˜E,1))*pc(2)),

and so on for i=3:N.

In some embodiments, the child fraction may be determined. The childfraction may refer to the proportion of sequences in a mixture of DNAthat originate from the child. In the context of non-invasive prenataldiagnosis, the child fraction may refer to the proportion of sequencesin the maternal plasma that originate from the fetus or the portion ofthe placenta with fetal genotype. It may refer to the child fraction ina sample of DNA that has been prepared from the maternal plasma, and maybe enriched in fetal DNA. One purpose of determining the child fractionin a sample of DNA is for use in an algorithm that can make ploidy callson the fetus, therefore, the child fraction could refer to whateversample of DNA was analyzed by sequencing for the purpose of non-invasiveprenatal diagnosis.

Some of the algorithms presented in this disclosure that are part of amethod of non-invasive prenatal aneuploidy diagnosis assume a knownchild fraction, which may not always the case. In an embodiment, it ispossible to find the most likely child fraction by maximizing thelikelihood for disomy on selected chromosomes, with or without thepresence of the parental data

In particular, suppose that LIK(D|H11, cf, chr)=log likelihood asdescribed above, for the disomy hypothesis, and for child fraction cf onchromosome chr. For selected chromosomes in Cset (usually 1:16), assumedto be euploid, the full likelihood is:

LIK(cf)=Σ_(chr∈Cset) Lik(D|H11,cf,chr)

The most likely child fraction (cf*) is derived as

${cf}^{*} = {\underset{cf}{\arg \; \max}\mspace{11mu} {{{LIK}({cf})}.}}$

It is possible to use any set of chromosomes. It is also possible toderive child fraction without assuming euploidy on the referencechromosomes. Using this method it is possible to determine the childfraction for any of the following situations: (1) one has array data onthe parents and shotgun sequencing data on the maternal plasma; (2) onehas array data on the parents and targeted sequencing data on thematernal plasma; (3) one has targeted sequencing data on both theparents and maternal plasma; (4) one has targeted sequencing data onboth the mother and the maternal plasma fraction; (5) one has targetedsequencing data on the maternal plasma fraction; (6) other combinationsof parental and child fraction measurements.

In some embodiments the informatics method may incorporate datadropouts; this may result in ploidy determinations of higher accuracy.Elsewhere in this disclosure it has been assumed that the probability ofgetting an A is a direct function of the true mother genotype, the truechild genotype, the fraction of the child in the mixture, and the childcopy number. It is also possible that mother or child alleles can dropout, for example instead of measuring true child AB in the mixture, itmay be the case that only sequences mapping to allele A are measured.One may denote the parent dropout rate for genomic ILLUMINA data d_(pg),parent dropout rate for sequence data d_(ps) and child dropout rate forsequence data d_(cs). In some embodiments, the mother dropout rate maybe assumed to be zero, and child dropout rates are relatively low; inthis case, the results are not severely affected by dropouts. In someembodiments the possibility of allele dropouts may be sufficiently largethat they result in a significant effect of the predicted ploidy call.For such a case, allele dropouts have been incorporated into thealgorithm here:

Parent SNP array data dropouts: For mother genomic data M, suppose thatthe genotype after the dropout is m_(d), then

${P\left( {\left. M \middle| m \right.,i} \right)} = {\sum\limits_{m_{d}}{{P\left( {\left. M \middle| m_{d} \right.,i} \right)}{P\left( m_{d} \middle| m \right)}}}$

where

${P\left( {\left. M \middle| m_{d} \right.,i} \right)} = \left\{ \begin{matrix}1 & {m_{i} = m_{d}} \\0 & {m_{i} \neq m_{d}}\end{matrix} \right.$

as before, and P(m_(d)|m) is the likelihood of genotype m_(d) after thepossible dropout given the true genotype m, defined as below, fordropout rate d

md m AA AB BB A B nocall AA (1-d){circumflex over ( )}2 0 0 2d(1-d) 0d{circumflex over ( )}2 AB 0 (1-d){circumflex over ( )}2 0  d(1-d) d(1-d) d{circumflex over ( )}2 BB 0 0 (1-d){circumflex over ( )}2 02d(1-d) d{circumflex over ( )}2A similar equation applies for father SNP array data.

Parent sequence data dropouts: For mother sequence data SM

${P\left( {{{SM}m},i} \right)} = {\sum\limits_{m_{d}}^{\;}{{P_{Xm_{d}}\left( {am}_{i} \right)}{P\left( {m_{d}m} \right)}}}$

where P(m_(d)|m) is defined as in previous section and P_(X|m) _(d)(am_(i)) probability from a binomial distribution is defined as beforein the parent data likelihood section. A similar equation applies to thepaternal sequence data.

Free Floating DNA Sequence Data Dropout:

${P\left( {{Sm},c,H,{cf},i} \right)} = {\sum\limits_{m_{d},c_{d}}^{\;}{{P\left( {{S{\mu \left( {m_{d},c_{d},{cf},H} \right)}},i} \right)}{P\left( {m_{d}m} \right)}{P\left( {c_{d}c} \right)}}}$

where P(S|μ(m_(d), c_(d), cf, H), i) is as defined in the section onfree floating data likelihood.

In an embodiment, p(m_(d)|m) is the probability of observed mothergenotype m_(d), given true mother genotype m, assuming dropout rated_(ps), and p(c_(d)|c) is the probability of observed child genotypec_(d), given true child genotype c, assuming dropout rate d_(cs). IfnA_(T)=number of A alleles in true genotype c, nA_(D)=number of Aalleles in observed genotype c_(d), where nA_(T)≥nA_(D), and similarlynB_(T)=number of B alleles in true genotype c, nB_(D)=number of Balleles in observed genotype c_(d), where nB_(T)≥nB_(D) and d=dropoutrate, then

${p\left( {c_{d}c} \right)} = {\begin{pmatrix}{n\; A_{T}} \\{n\; A_{D}}\end{pmatrix}*d^{{n\; A_{T}} - {n\; A_{D}}}*\left( {1 - d} \right)^{n\; A_{D}}*\begin{pmatrix}{n\; B_{T}} \\{n\; B_{D}}\end{pmatrix}*d^{{n\; B_{T}} - {n\; B_{D}}}*\left( {1 - d} \right)^{n\; B_{D}}}$

In an embodiment, the informatics method may incorporate random andconsistent bias. In an ideal word there is no per SNP consistentsampling bias or random noise (in addition to the binomial distributionvariation) in the number of sequence counts. In particular, on SNP i,for mother genotype m, true child genotype c and child fraction cf, andX=the number of A's in the set of (A+B) reads on SNP i, X acts like aX˜Binomial(p, A+B), where p=μ(m, c, cf, H)=true probability of Acontent.

In an embodiment, the informatics method may incorporate random bias. Asis often the case, suppose that there is a bias in the measurements, sothat the probability of getting an A on this SNP is equal to q, which isa bit different than p as defined above. How much different p is from qdepends on the accuracy of the measurement process and number of otherfactors and can be quantified by standard deviations of q away from p.In an embodiment, it is possible to model q as having a betadistribution, with parameters α,β depending on the mean of thatdistribution being centered at p, and some specified standard deviations. In particular, this gives X|q˜Bin(q, D_(i)), where q˜Beta(α,β). If welet E(q)=p, V(q)=s², and parameters α,β can be derived as α=pN,β=(1−p)N, where

$N = {\frac{p\left( {1 - p} \right)}{s^{2}} - 1.}$

This is the definition of a beta-binomial distribution, where one issampling from a binomial distribution with variable parameter q, where qfollows a beta distribution with mean p. So, in a setup with no bias, onSNP i, the parent sequence data (SM) probability assuming true mothergenotype (m), given mother sequence A count on SNP i (am_(i)) and mothersequence B count on SNP i (bm_(i)) may be calculated as:

P(SM|m,i)=P _(X|m)(am _(i)) where X|m˜Binom(p _(m)(A),am _(i) +bm _(i))

Now, including random bias with standard deviation s, this becomes:

X|m˜BetaBinom(p _(m)(A),am _(i) +bm _(i) ,s)

In the case with no bias, the maternal plasma DNA sequence data (S)probability assuming true mother genotype (m), true child genotype (c),child fraction (cf), assuming child hypothesis H, given free floatingDNA sequence A count on SNP i (a_(i)) and free floating sequence B counton SNP i (b_(i)) may be calculated as

P(S|m,c,cf,H,i)=P _(x)(a _(i))

where X˜Binom(p(A), a_(i)+b_(i)) with p(A)=μ(m, c, cf, H).

In an embodiment, including random bias with standard deviation s, thisbecomes X˜BetaBinom(p(A),a_(i)+b_(i),s), where the amount of extravariation is specified by the deviation parameter s, or equivalently N.The smaller the value of s (or the larger the value of N) the closerthis distribution is to the regular binomial distribution. It ispossible to estimate the amount of bias, i.e. estimate N above, fromunambiguous contexts AA|AA, BB|BB, AA|BB, BB|AA and use estimated N inthe above probability. Depending on the behavior of the data, N may bemade to be a constant irrespective of the depth of read a_(i)+b_(i), ora function of a_(i)+b_(i), making bias smaller for larger depths ofread.

In an embodiment, the informatics method may incorporate consistentper-SNP bias. Due to artifacts of the sequencing process, some SNPs mayhave consistently lower or higher counts irrespective of the true amountof A content. Suppose that SNP i consistently adds a bias of w_(i)percent to the number of A counts. In some embodiments, this bias can beestimated from the set of training data derived under same conditions,and added back in to the parent sequence data estimate as:

P(SM|m,i)=P _(X|m)(am _(i)) where X|m˜BetaBinom(p _(m)(A)+w _(i) ,am_(i) +bm _(i) ,s)

and with the free floating DNA sequence data probability estimate as:

P(S|m,c,cf,H,i)=P _(x)(a _(i)) where X˜BetaBinom(p(A)+w _(i) ,a _(i) +b_(i) ,s),

In some embodiments, the method may be written to specifically take intoaccount additional noise, differential sample quality, differential SNPquality, and random sampling bias. An example of this is given here.This method has been shown to be particularly useful in the context ofdata generated using the massively multiplexed mini-PCR protocol, andwas used in Experiments 7 through 13. The method involves several stepsthat each introduce different kind of noise and/or bias to the finalmodel:

(1) Suppose the first sample that comprises a mixture of maternal andfetal DNA contains an original amount of DNA of size=N₀ molecules,usually in the range 1,000-40,000, where p=true % refs

(2) In the amplification using the universal ligation adaptors, assumethat N₁ molecules are sampled; usually N₁˜N₀/2 molecules and randomsampling bias is introduced due to sampling. The amplified sample maycontain a number of molecules N₂ where N₂>>N₁. Let X₁ represent theamount of reference loci (on per SNP basis) out of N₁ sampled molecules,with a variation in p₁=X₁/N₁ that introduces random sampling biasthroughout the rest of protocol. This sampling bias is included in themodel by using a Beta-Binomial (BB) distribution instead of using asimple Binomial distribution model. Parameter N of the Beta-Binomialdistribution may be estimated later on per sample basis from trainingdata after adjusting for leakage and amplification bias, on SNPs with0<p<1. Leakage is the tendency for a SNP to be read incorrectly.

(3) The amplification step will amplify any allelic bias, thusamplification bias introduced due to possible uneven amplification.Suppose that one allele at a locus is amplified f times another alleleat that locus is amplified g times, where f=ge^(b), where b=0 indicatesno bias. The bias parameter, b, is centered at 0, and indicates how muchmore or less the A allele get amplified as opposed to the B allele on aparticular SNP. The parameter b may differ from SNP to SNP. Biasparameter b may be estimated on per SNP basis, for example from trainingdata.

(4) The sequencing step involves sequencing a sample of amplifiedmolecules. In this step there may be leakage, where leakage is thesituation where a SNP is read incorrectly. Leakage may result from anynumber of problems, and may result in a SNP being read not as thecorrect allele A, but as another allele B found at that locus or as anallele C or D not typically found at that locus. Suppose the sequencingmeasures the sequence data of a number of DNA molecules from anamplified sample of size N₃, where N₃<N₂. In some embodiments, N₃ may bein the range of 20,000 to 100,000; 100,000 to 500,000; 500,000 to4,000,000; 4,000,000 to 20,000,000; or 20,000,000 to 100,000,000. Eachmolecule sampled has a probability p_(g) of being read correctly, inwhich case it will show up correctly as allele A. The sample will beincorrectly read as an allele unrelated to the original molecule withprobability 1−p_(g), and will look like allele A with probability p_(r),allele B with probability p_(m) or allele C or allele D with probabilityp_(o), where p_(r)+p_(m)+p_(o)=1. Parameters p_(g), p_(r), p_(m), p_(o)are estimated on per SNP basis from the training data.

Different protocols may involve similar steps with variations in themolecular biology steps resulting in different amounts of randomsampling, different levels of amplification and different leakage bias.The following model may be equally well applied to each of these cases.The model for the amount of DNA sampled, on per SNP basis, is given by:

X ₃˜BetaBinomial(L(F(p,b),p _(r) ,p _(g)),N*H(p,b))

where p=the true amount of reference DNA, b=per SNP bias, and asdescribed above, p_(g) is the probability of a correct read, P_(r) isthe probability of read being read incorrectly but serendipitouslylooking like the correct allele, in case of a bad read, as describedabove, and:

F(p,b),pe ^(b)/(pe ^(b)+(1−p)),H(p,b)=(e ^(b) p+(1−p))² /e ^(b) ,L(p,p_(r) ,p _(g))=p*p _(g) +p _(r)*(1−p _(g)).

In some embodiments, the method uses a Beta-Binomial distributioninstead of a simple binomial distribution; this takes care of the randomsampling bias. Parameter N of the Beta-Binomial distribution isestimated on per sample basis on an as needed basis. Using biascorrection F(p,b), H(p,b), instead of just p, takes care of theamplification bias. Parameter b of the bias is estimated on per SNPbasis from training data ahead of time.

In some embodiments the method uses leakage correction L(p,p_(r),p_(g)),instead of just p; this takes care of the leakage bias, i.e. varying SNPand sample quality. In some embodiments, parameters p_(g), p_(r), p_(o)are estimated on per SNP basis from the training data ahead of time. Insome embodiments, the parameters p_(g), p_(r), p_(o) may be updated withthe current sample on the go, to account for varying sample quality.

The model described herein is quite general and can account for bothdifferential sample quality and differential SNP quality. Differentsamples and SNPs are treated differently, as exemplified by the factthat some embodiments use Beta-Binomial distributions whose mean andvariance are a function of the original amount of DNA, as well as sampleand SNP quality.

Platform Modeling

Consider a single SNP where the expected allele ratio present in theplasma is r (based on the maternal and fetal genotypes). The expectedallele ratio is defined as the expected fraction of A alleles in thecombined maternal and fetal DNA. For maternal genotype g_(m) and childgenotype g_(c), the expected allele ratio is given by equation 1,assuming that the genotypes are represented as allele ratios as well.

r=fg _(c)+(1−f)g _(m)  (1)

The observation at the SNP consists of the number of mapped reads witheach allele present, n_(a) and n_(b), which sum to the depth of read d.Assume that thresholds have already been applied to the mappingprobabilities and phred scores such that the mappings and alleleobservations can be considered correct. A phred score is a numericalmeasure that relates to the probability that a particular measurement ata particular base is wrong. In an embodiment, where the base has beenmeasured by sequencing, the phred score may be calculated from the ratioof the dye intensity corresponding to the called base to the dyeintensity of the other bases. The simplest model for the observationlikelihood is a binomial distribution which assumes that each of the dreads is drawn independently from a large pool that has allele ratio r.Equation 2 describes this model.

$\begin{matrix}{{P\left( {n_{a},{n_{b}r}} \right)} = {{p_{bino}\left( {{n_{a};{n_{a} + n_{b}}},r} \right)} = {\begin{pmatrix}{n_{a} + n_{b}} \\n_{a}\end{pmatrix}{r^{n_{a}}\left( {1 - r} \right)}^{n_{b}}}}} & (2)\end{matrix}$

The binomial model can be extended in a number of ways. When thematernal and fetal genotypes are either all A or all B, the expectedallele ratio in plasma will be 0 or 1, and the binomial probability willnot be well-defined. In practice, unexpected alleles are sometimesobserved in practice. In an embodiment, it is possible to use acorrected allele ratio {circumflex over (r)}=1/(n_(a)+n_(b)) to allow asmall number of the unexpected allele. In an embodiment, it is possibleto use training data to model the rate of the unexpected alleleappearing on each SNP, and use this model to correct the expected alleleratio. When the expected allele ratio is not 0 or 1, the observed alleleratio may not converge with a sufficiently high depth of read to theexpected allele ratio due to amplification bias or other phenomena. Theallele ratio can then be modeled as a beta distribution centered at theexpected allele ratio, leading to a beta-binomial distribution forP(n_(a), n_(b)|r) which has higher variance than the binomial.

The platform model for the response at a single SNP will be defined asF(a, b, g_(c), g_(m), f) (3), or the probability of observing n_(a)=aand n_(b)=b given the maternal and fetal genotypes, which also dependson the fetal fraction through equation 1. The functional form of F maybe a binomial distribution, beta-binomial distribution, or similarfunctions as discussed above.

F(a,b,g _(c) ,g _(m) ,f)=P(n _(a) =a,n _(b) =b|g _(c) ,g _(m) ,f)=P(n_(a) =a,n _(b) =b|r(g _(c) ,g _(m) ,f))  (3)

In an embodiment, the child fraction may be determined as follows. Amaximum likelihood estimate of the fetal fraction f for a prenatal testmay be derived without the use of paternal information. This may berelevant where the paternal genetic data is not available, for examplewhere the father of record is not actually the genetic father of thefetus. The fetal fraction is estimated from the set of SNPs where thematernal genotype is 0 or 1, resulting in a set of only two possiblefetal genotypes. Define S₀ as the set of SNPs with maternal genotype 0and S₁ as the set of SNPs with maternal genotype 1. The possible fetalgenotypes on So are 0 and 0.5, resulting in a set of possible alleleratios R₀(f)={0,f/2}. Similarly, R₁(f)={1−f/2, 1}. This method can betrivially extended to include SNPs where maternal genotype is 0.5, butthese SNPs will be less informative due to the larger set of possibleallele ratios.

Define N_(a0) and N_(b0) as the vectors formed by n_(as) and n_(bs) forSNPs s in S₀, and N_(a1) and N_(b1) similarly for S₁. The maximumlikelihood estimate f off is defined by equation 4.

{circumflex over (f)}=arg max_(f) P(N _(a0) ,N _(b0) |f)P(N _(a1) ,N_(b1) |f)  (4)

Assuming that the allele counts at each SNP are independent conditionedon the SNP's plasma allele ratio, the probabilities can be expressed asproducts over the SNPs in each set (5).

P(N _(a0) ,N _(b0) |f)=Π_(s∈S) ₀ P(n _(as) ,n _(bs) |f)

P(N _(a1) ,N _(b1) |f)=Π_(s∈S) ₁ P(n _(as) ,n _(bs) |f)  (5)

The dependence on f is through the sets of possible allele ratios R₀(f)and R₁(f). The SNP probability P(n_(as), n_(bs)|f) can be approximatedby assuming the maximum likelihood genotype conditioned on f. Atreasonably high fetal fraction and depth of read, the selection of themaximum likelihood genotype will be high confidence. For example, atfetal fraction of 10 percent and depth of read of 1000, consider a SNPwhere the mother has genotype zero. The expected allele ratios are 0 and5 percent, which will be easily distinguishable at sufficiently highdepth of read. Substitution of the estimated child genotype intoequation 5 results in the complete equation (6) for the fetal fractionestimate.

$\begin{matrix}{\hat{f} = {\arg \; {\max_{f}\left( {\prod_{{s\epsilon S}_{0}}\left( {\max\limits_{r_{s}\epsilon \; {R_{0}{(f)}}}{{P\left( {n_{as},{n_{bs}r_{s}}} \right)}{\prod_{{s\epsilon S}_{1}}\left( {\max\limits_{r_{s}\epsilon \; {R_{1}{(f)}}}{P\left( {n_{as},{n_{bs}r_{s}}} \right)}} \right\rbrack}}} \right.} \right.}}} & (6)\end{matrix}$

The fetal fraction must be in the range [0, 1] and so the optimizationcan be easily implemented by a constrained one-dimensional search.

In the presence of low depth of read or high noise level, it may bepreferable not to assume the maximum likelihood genotype, which mayresult in artificially high confidences. Another method would be to sumover the possible genotypes at each SNP, resulting in the followingexpression (7) for P(n_(a), n_(b)|f) for a SNP in S₀. The priorprobability P(r) could be assumed uniform over R₀(f), or could be basedon population frequencies. The extension to group S₁ is trivial.

P(n _(a) ,n _(b) |f)=Σ_(r∈R) ₀ _((f)) P(n _(a) ,n _(a) |r)P(r)  (7)

In some embodiments the probabilities may be derived as follows. Aconfidence can be calculated from the data likelihoods of the twohypotheses H_(t) and H_(f). The likelihood of each hypothesis is derivedbased on the response model, the estimated fetal fraction, the mothergenotypes, allele population frequencies, and the plasma allele counts.

Define the following notation:

G_(m), G_(c) true maternal and child genotypes G_(af), G_(tf) truegenotypes of alleged father and of true father G(g_(c), g_(m), g_(tf)) =inheritence probabilities P(G_(c) = g_(c)|G_(m) = g_(m), G_(tf) =g_(tf)) P(g) = P(G_(tf) = g) population frequency of genotype g atparticular SNP

Assuming that the observation at each SNP is independent conditioned onthe plasma allele ratio, the likelihood of a paternity hypothesis is theproduct of the likelihoods on the SNPs. The following equations derivethe likelihood for a single SNP. Equation 8 is a general expression forthe likelihood of any hypothesis h, which will then be broken down intothe specific cases of H_(t) and H_(f).

$\quad\begin{matrix}\begin{matrix}{{P\left( {n_{a},{n_{b}h},G_{m},G_{tf},f} \right)} = {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{P\left( {n_{a},{{n_{b}G_{c}} =}} \right.}}} \\{\left. {g_{c},G_{m},G_{tf},h,f} \right){P\left( {{G_{c} = g_{c}},G_{m},G_{tf},h,f} \right)}} \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{P\left( {n_{a},{{n_{b}G_{c}} = g_{c}},G_{m},f} \right)}}} \\{{P\left( {{G_{c} = {g_{c}G_{m}}},G_{tf},h} \right)}} \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}}} \\{{P\left( {{G_{c} = {g_{c}G_{m}}},G_{tf},h} \right)}}\end{matrix} & (8)\end{matrix}$

In the case of H_(t), the alleged father is the true father and thefetal genotypes are inherited from the maternal genotypes and allegedfather genotypes according to equation 9.

$\begin{matrix}\begin{matrix}{{P\left( {n_{a},{n_{b}H_{t}},G_{m},G_{tf},f} \right)} = {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}{P\left( {G_{c} =} \right.}}}} \\\left. {{g_{c}G_{m}},G_{tf},H_{t}} \right) \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}}} \\{{G\left( {g_{c},G_{m},G_{{tf}\;}} \right)}}\end{matrix} & (9)\end{matrix}$

In the case of H_(f), the alleged father is not the true father. Thebest estimate of the true father genotypes are given by the populationfrequencies at each SNP. Thus, the probabilities of child genotypes aredetermined by the known mother genotypes and the population frequencies,as in equation 10.

$\quad\begin{matrix}{{P\left( {n_{a},{n_{b}H_{t}},G_{m},G_{tf},f} \right)} = {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}}} \\{{P\left( {{G_{c} = {g_{c}G_{m}}},G_{tf},H_{f}} \right)}} \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}}} \\{{P\left( {G_{c} = {g_{c}G_{m}}} \right)}} \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}\sum_{g_{tf}{\epsilon {({0,0.5,1})}}}}} \\{{{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}{P\left( {G_{c} =} \right.}}} \\{\left. {{g_{c}G_{m}},{G_{tf} = g_{tf}}} \right){P\left( {G_{tf} = g_{tf}} \right)}} \\{= {\sum_{g_{c}{\epsilon {({0,0.5,1})}}}\sum_{g_{tf}{\epsilon {({0,0.5,1})}}}}} \\{{{F\left( {n_{a},n_{b},g_{c},g_{m},f} \right)}{G\left( {g_{c},G_{m},g_{tf}} \right)}{P\left( g_{tf} \right)}}}\end{matrix}$

The confidence C_(p) on correct paternity is calculated from the productover SNPs of the two likelihoods using Bayes rule (11).

$\begin{matrix}{{Cp} = \frac{\prod_{s}{P\left( {n_{as},{n_{bs}H_{t}},G_{ms},G_{tf},f} \right)}}{\begin{matrix}{{\prod_{s}{P\left( {n_{as},{n_{bs}H_{t}},G_{ms},G_{tf},f} \right)}} +} \\{\prod_{s}{P\left( {n_{as},{n_{bs}H_{f}},G_{ms},G_{tf},f} \right)}}\end{matrix}}} & (11)\end{matrix}$

Maximum Likelihood Model Using Percent Fetal Fraction

Determining the ploidy status of a fetus by measuring the free floatingDNA contained in maternal serum, or by measuring the genotypic materialin any mixed sample, is a non-trivial exercise. There are a number ofmethods, for example, performing a read count analysis where thepresumption is that if the fetus is trisomic at a particular chromosome,then the overall amount of DNA from that chromosome found in thematernal blood will be elevated with respect to a reference chromosome.One way to detect trisomy in such fetuses is to normalize the amount ofDNA expected for each chromosome, for example, according to the numberof SNPs in the analysis set that correspond to a given chromosome, oraccording to the number of uniquely mappable portions of the chromosome.Once the measurements have been normalized, any chromosomes for whichthe amount of DNA measured exceeds a certain threshold are determined tobe trisomic. This approach is described in Fan, et al. PNAS, 2008;105(42); pp. 16266-16271, and also in Chiu et al. BMJ 2011; 342:c7401.In the Chiu et al. paper, the normalization was accomplished bycalculating a Z score as follows:

Z score for percentage chromosome 21 in test case=((percentagechromosome 21 in test case)−(mean percentage chromosome 21 in referencecontrols))/(standard deviation of percentage chromosome 21 in referencecontrols).

These methods determine the ploidy status of the fetus using a singlehypothesis rejection method. However, they suffer from some significantshortcomings. Since these methods for determining ploidy in the fetusare invariant according to the percentage of fetal DNA in the sample,they use one cut off value; the result of this is that the accuracies ofthe determinations are not optimal, and those cases where the percentageof fetal DNA in the mixture are relatively low will suffer the worstaccuracies.

In an embodiment, a method of the present disclosure is used todetermine the ploidy state of the fetus involves taking into account thefraction of fetal DNA in the sample. In another embodiment of thepresent disclosure, the method involves the use of maximum likelihoodestimations. In an embodiment, a method of the present disclosureinvolves calculating the percent of DNA in a sample that is fetal orplacental in origin. In an embodiment, the threshold for callinganeuploidy is adaptively adjusted based on the calculated percent fetalDNA. In some embodiments, the method for estimating the percentage ofDNA that is of fetal origin in a mixture of DNA, comprises obtaining amixed sample that comprises genetic material from the mother, andgenetic material from the fetus, obtaining a genetic sample from thefather of the fetus, measuring the DNA in the mixed sample, measuringthe DNA in the father sample, and calculating the percentage of DNA thatis of fetal origin in the mixed sample using the DNA measurements of themixed sample, and of the father sample.

In an embodiment of the present disclosure, the fraction of fetal DNA,or the percentage of fetal DNA in the mixture can be measured. In someembodiments the fraction can be calculated using only the genotypingmeasurements made on the maternal plasma sample itself, which is amixture of fetal and maternal DNA. In some embodiments the fraction maybe calculated also using the measured or otherwise known genotype of themother and/or the measured or otherwise known genotype of the father. Insome embodiments the percent fetal DNA may be calculated using themeasurements made on the mixture of maternal and fetal DNA along withthe knowledge of the parental contexts. In an embodiment, the fractionof fetal DNA may be calculated using population frequencies to adjustthe model on the probability on particular allele measurements.

In an embodiment of the present disclosure, a confidence may becalculated on the accuracy of the determination of the ploidy state ofthe fetus. In an embodiment, the confidence of the hypothesis ofgreatest likelihood (H_(major)) may be calculated as (1−H_(major))/Σ(allH). It is possible to determine the confidence of a hypothesis if thedistributions of all of the hypotheses are known. It is possible todetermine the distribution of all of the hypotheses if the parentalgenotype information is known. It is possible to calculate a confidenceof the ploidy determination if the knowledge of the expecteddistribution of data for the euploid fetus and the expected distributionof data for the aneuploid fetus are known. It is possible to calculatethese expected distributions if the parental genotype data are known. Inan embodiment one may use the knowledge of the distribution of a teststatistic around a normal hypothesis and around an abnormal hypothesisto determine both the reliability of the call as well as refine thethreshold to make a more reliable call. This is particularly useful whenthe amount and/or percent of fetal DNA in the mixture is low. It willhelp to avoid the situation where a fetus that is actually aneuploid isfound to be euploid because a test statistic, such as the Z statisticdoes not exceed a threshold that is made based on a threshold that isoptimized for the case where there is a higher percent fetal DNA.

In an embodiment, a method disclosed herein can be used to determine afetal aneuploidy by determining the number of copies of maternal andfetal target chromosomes in a mixture of maternal and fetal geneticmaterial. This method may entail obtaining maternal tissue comprisingboth maternal and fetal genetic material; in some embodiments thismaternal tissue may be maternal plasma or a tissue isolated frommaternal blood. This method may also entail obtaining a mixture ofmaternal and fetal genetic material from said maternal tissue byprocessing the aforementioned maternal tissue. This method may entaildistributing the genetic material obtained into a plurality of reactionsamples, to randomly provide individual reaction samples that comprise atarget sequence from a target chromosome and individual reaction samplesthat do not comprise a target sequence from a target chromosome, forexample, performing high throughput sequencing on the sample. Thismethod may entail analyzing the target sequences of genetic materialpresent or absent in said individual reaction samples to provide a firstnumber of binary results representing presence or absence of apresumably euploid fetal chromosome in the reaction samples and a secondnumber of binary results representing presence or absence of a possiblyaneuploid fetal chromosome in the reaction samples. Either of the numberof binary results may be calculated, for example, by way of aninformatics technique that counts sequence reads that map to aparticular chromosome, to a particular region of a chromosome, to aparticular locus or set of loci. This method may involve normalizing thenumber of binary events based on the chromosome length, the length ofthe region of the chromosome, or the number of loci in the set. Thismethod may entail calculating an expected distribution of the number ofbinary results for a presumably euploid fetal chromosome in the reactionsamples using the first number. This method may entail calculating anexpected distribution of the number of binary results for a presumablyaneuploid fetal chromosome in the reaction samples using the firstnumber and an estimated fraction of fetal DNA found in the mixture, forexample, by multiplying the expected read count distribution of thenumber of binary results for a presumably euploid fetal chromosome by(1+n/2) where n is the estimated fetal fraction. In some embodiments,the sequence reads may be treated at probabilistic mappings rather thanbinary results; this method would yield higher accuracies, but requiremore computing power. The fetal fraction may be estimated by a pluralityof methods, some of which are described elsewhere in this disclosure.This method may involve using a maximum likelihood approach to determinewhether the second number corresponds to the possibly aneuploid fetalchromosome being euploid or being aneuploid. This method may involvecalling the ploidy status of the fetus to be the ploidy state thatcorresponds to the hypothesis with the maximum likelihood of beingcorrect given the measured data.

Note that the use of a maximum likelihood model may be used to increasethe accuracy of any method that determines the ploidy state of a fetus.Similarly, a confidence maybe calculated for any method that determinesthe ploidy state of the fetus. The use of a maximum likelihood modelwould result in an improvement of the accuracy of any method where theploidy determination is made using a single hypothesis rejectiontechnique. A maximum likelihood model may be used for any method where alikelihood distribution can be calculated for both the normal andabnormal cases. The use of a maximum likelihood model implies theability to calculate a confidence for a ploidy call.

Further Discussion of the Method

In an embodiment, a method disclosed herein utilizes a quantitativemeasure of the number of independent observations of each allele at apolymorphic locus, where this does not involve calculating the ratio ofthe alleles. This is different from methods, such as some microarraybased methods, which provide information about the ratio of two allelesat a locus but do not quantify the number of independent observations ofeither allele. Some methods known in the art can provide quantitativeinformation regarding the number of independent observations, but thecalculations leading to the ploidy determination utilize only the alleleratios, and do not utilize the quantitative information. To illustratethe importance of retaining information about the number of independentobservations consider the sample locus with two alleles, A and B. In afirst experiment twenty A alleles and twenty B alleles are observed, ina second experiment 200 A alleles and 200 B alleles are observed. Inboth experiments the ratio (A/(A+B)) is equal to 0.5, however the secondexperiment conveys more information than the first about the certaintyof the frequency of the A or B allele. The instant method, rather thanutilizing the allele ratios, uses the quantitative data to moreaccurately model the most likely allele frequencies at each polymorphiclocus.

In an embodiment, the instant methods build a genetic model foraggregating the measurements from multiple polymorphic loci to betterdistinguish trisomy from disomy and also to determine the type oftrisomy. Additionally, the instant method incorporates genetic linkageinformation to enhance the accuracy of the method. This is in contrastto some methods known in the art where allele ratios are averaged acrossall polymorphic loci on a chromosome. The method disclosed hereinexplicitly models the allele frequency distributions expected in disomyas well as and trisomy resulting from nondisjunction during meiosis I,nondisjunction during meiosis II, and nondisjunction during mitosisearly in fetal development. To illustrate why this is important, ifthere were no crossovers nondisjunction during meiosis I would result atrisomy in which two different homologs were inherited from one parent;nondisjunction during meiosis II or during mitosis early in fetaldevelopment would result in two copies of the same homolog from oneparent. Each scenario results in different expected allele frequenciesat each polymorphic locus and also at all physically linked loci (i.e.loci on the same chromosome) considered jointly. Crossovers, whichresult in the exchange of genetic material between homologs, make theinheritance pattern more complex, but the instant method accommodatesfor this by using genetic linkage information, i.e. recombination rateinformation and the physical distance between loci. To betterdistinguish between meiosis I nondisjunction and meiosis II or mitoticnondisjunction the instant method incorporates into the model anincreasing probability of crossover as the distance from the centromereincreases. Meiosis II and mitotic nondisjunction can be distinguished bythe fact that mitotic nondisjunction typically results in identical ornearly identical copies of one homolog while the two homologs presentfollowing a meiosis II nondisjunction event often differ due to one ormore crossovers during gametogenesis.

In an embodiment, a method of the present disclosure may not determinethe haplotypes of the parents if disomy is assumed. In an embodiment, incase of trisomy, the instant method can make a determination about thehaplotypes of one or both parents by using the fact that plasma takestwo copies from one parent, and parent phase information can bedetermined by noting which two copies have been inherited from theparent in question. In particular, a child can inherit either two of thesame copies of the parent (matched trisomy) or both copies of the parent(unmatched trisomy). At each SNP one can calculate the likelihood of thematched trisomy and of the unmatched trisomy. A ploidy calling methodthat does not use the linkage model accounting for crossovers wouldcalculate the overall likelihood of the trisomy as a simple weightedaverage of the matched and unmatched trisomies over all chromosomes.However, due to the biological mechanisms that result in disjunctionerror and crossing over, trisomy can change from matched to unmatched(and vice versa) on a chromosome only if a crossover occurs. The instantmethod probabilistically takes into account the likelihood of crossover,resulting in ploidy calls that are of greater accuracy than thosemethods that do not.

In an embodiment, a reference chromosome is used to determine the childfraction and noise level amount or probability distribution. In anembodiment, the child fraction, noise level, and/or probabilitydistribution is determined using only the genetic information availablefrom the chromosome whose ploidy state is being determined. The instantmethod works without the reference chromosome, as well as without fixingthe particular child fraction or noise level. This is a significantimprovement and point of differentiation from methods known in the artwhere genetic data from a reference chromosome is necessary to calibratethe child fraction and chromosome behavior.

In an embodiment where a reference chromosome is not needed to determinethe fetal fraction, determining the hypothesis is done as follows:

$H^{*} = {\underset{H}{argmax}\; {{LIK}\left( {DH} \right)}*{{priorprob}(H)}}$

With the algorithm with reference chromosome, one typically assumes thatthe reference chromosome is a disomy, and then one may either (a) fixthe most likely child fraction and random noise level N based on thisassumption and reference chromosome data:

$\left\lbrack {{cfr}^{*},N^{*}} \right\rbrack = {\underset{{cfr},N}{argmax}\; {{LIK}\left( {{{D\left( {{ref}.{chrom}} \right)}{H\; 11}},{cfr},N} \right)}}$

And then reduce

LIK(D|H)=LIK(D|H,cfr*,N*)

or (b) estimate the child fraction and noise level distribution based onthis assumption and reference chromosome data. In particular, one wouldnot fix just one value for cfr and N, but assign probability p(cfr, N)for the wider range of possible cfr, N values:

p(cfr,N)˜LIK(D(ref·chrom)|H11,cfr,N)*priorprob(cfr,N)

where priorprob(cfr, N) is the prior probability of particular childfraction and noise level, determined by prior knowledge and experiments.If desired, just uniform over the range of cfr, N. One may then write:

${{LIK}\left( {DH} \right)} = {\sum\limits_{{cfr},N}{{{LIK}\left( {{DH},{cfr},N} \right)}*{p\left( {{cfr},N} \right)}}}$

Both methods above give good results.

Note that in some instances using a reference chromosome is notdesirable, possible or feasible. In such a case, it is possible toderive the best ploidy call for each chromosome separately. Inparticular:

${{LIK}\left( {DH} \right)} = {\sum\limits_{{cfr},N}{{{LIK}\left( {{DH},{cfr},N} \right)}*{p\left( {{cfr},{NH}} \right)}}}$

p(cfr, N|H) may be determined as above, for each chromosome separately,assuming hypothesis H, not just for the reference chromosome assumingdisomy. It is possible, using this method, to keep both noise and childfraction parameters fixed, fix either of the parameters, or keep bothparameters in probabilistic form for each chromosome and eachhypothesis.

Measurements of DNA are noisy and/or error prone, especiallymeasurements where the amount of DNA is small, or where the DNA is mixedwith contaminating DNA. This noise results in less accurate genotypicdata, and less accurate ploidy calls. In some embodiments, platformmodeling or some other method of noise modeling may be used to counterthe deleterious effects of noise on the ploidy determination. Theinstant method uses a joint model of both channels, which accounts forthe random noise due to the amount of input DNA, DNA quality, and/orprotocol quality.

This is in contrast to some methods known in the art where the ploidydeterminations are made using the ratio of allele intensities at alocus. This method precludes accurate SNP noise modeling. In particular,errors in the measurements typically do not specifically depend on themeasured channel intensity ratio, which reduces the model to usingone-dimensional information. Accurate modeling of noise, channel qualityand channel interaction requires a two-dimensional joint model, whichcan not be modeled using allele ratios.

In particular, projecting two channel information to the ratio r wheref(x,y) is r=x/y, does not lend itself to accurate channel noise and biasmodeling. Noise on a particular SNP is not a function of the ratio, i.e.noise(x,y)≠f(x,y) but is in fact a joint function of both channels. Forexample, in the binomial model, noise of the measured ratio has avariance of r(1−r)/(x+y) which is not a function purely of r. In such amodel, where any channel bias or noise is included, suppose that on SNPi, the observed channel X value is x=a_(i)X+b_(i), where X is the truechannel value, b_(i) is the extra channel bias and random noise.Similarly, suppose that y=c_(i)Y+d_(i). The observed ratio r=x/y can notaccurately predict the true ratio X/Y or model the leftover noise, since(aiX+bi)/(ciY+di) is not a function of X/Y.

The method disclosed herein describes an effective way to model noiseand bias using joint binomial distributions of all of the measurementchannels individually. Relevant equations may be found elsewhere in thedocument in sections which speaks of per SNP consistent bias, P(good)and P(ref|bad), P(mut|bad) which effectively adjust SNP behavior. In anembodiment, a method of the present disclosure uses a BetaBinomialdistribution, which avoids the limiting practice of relying on theallele ratios only, but instead models the behavior based on bothchannel counts.

In an embodiment, a method disclosed herein can call the ploidy of agestating fetus from genetic data found in maternal plasma by using allavailable measurements. In an embodiment, a method disclosed herein cancall the ploidy of a gestating fetus from genetic data found in maternalplasma by using the measurements from only a subset of parentalcontexts. Some methods known in the art only use measured genetic datawhere the parental context is from the AA|BB context, that is, where theparents are both homozygous at a given locus, but for a differentallele. One problem with this method is that a small proportion ofpolymorphic loci are from the AA|BB context, typically less than 10%. Inan embodiment of a method disclosed herein, the method does not usegenetic measurements of the maternal plasma made at loci where theparental context is AA|BB. In an embodiment, the instant method usesplasma measurements for only those polymorphic loci with the AAIAB,AB|AA, and ABIAB parental context.

Some methods known in the art involve averaging allele ratios from SNPsin the AA|BB context, where both parent genotypes are present, and claimto determine the ploidy calls from the average allele ratio on theseSNPs. This method suffers from significant inaccuracy due differentialSNP behavior. Note that this method assumes that have both parentgenotypes are known. In contrast, in some embodiments, the instantmethod uses a joint channel distribution model that does not assume thepresence of either of the parents, and does not assume the uniform SNPbehavior. In some embodiments, the instant method accounts for thedifferent SNP behavior/weighing. In some embodiments, the instant methoddoes not require the knowledge of one or both parental genotypes. Anexample of how the instant method may accomplish this follows:

In some embodiments, the log likelihood of a hypothesis may bedetermined on a per SNP basis. On a particular SNP i, assuming fetalploidy hypothesis H and percent fetal DNA cf, the log likelihood ofobserved data D is defined as:

${{LIK}\left( {{DH},i} \right)} = {{\log \; {P\left( {{DH},{cf},i} \right)}} = {\log\left( {\sum\limits_{m,f,c}{{P\left( {{Dm},f,c,H,{cf},i} \right)}{P\left( {{cm},f,H} \right)}{P\left( {mi} \right)}{P\left( {fi} \right)}}} \right)}}$

where m are possible true mother genotypes, f are possible true fathergenotypes, where m,f E {AA,AB,BB}, and where c are possible childgenotypes given the hypothesis H. In particular, for monosomy c {A, B},for disomy c∈{AA, AB, BB}, for trisomy c∈{AAA, AAB, ABB, BBB}. Note thatincluding parental genotypic data typically results in more accurateploidy determinations, however, parental genotypic data is not necessaryfor the instant method to work well.

Some methods known in the art involve averaging allele ratios from SNPswhere the mother is homozygous but a different allele is measured in theplasma (either AAIAB or AA|BB contexts), and claim to determine theploidy calls from the average allele ratio on these SNPs. This method isintended for cases where the paternal genotype is not available. Notethat it is questionable how accurately one can claim that plasma isheterozygous on a particular SNP without the presence of homozygous andopposite father BB: for cases with low child fraction, what looks likepresence of B allele could be just presence of noise; additionally, whatlooks like no B present could be simple allele drop out of the fetalmeasurements. Even in a case where one can actually determineheterozygosity of the plasma, this method will not be able todistinguish paternal trisomies. In particular, for SNPs where mother isAA, and where some B is measured in the plasma, if the father is GG, theresulting child genotype is AGG, resulting in an average ratio of 33% A(for child fraction=100%). But in the case where the father is AG, theresulting child genotype could be AGG for matched trisomy, contributingto the 33% A ratio, or AAG for unmatched trisomy, drawing the averageratio more toward 66% A. Given that many trisomies are on chromosomeswith crossovers, the overall chromosome can have anywhere between nounmatched trisomy and all unmatched trisomy, this ratio can varyanywhere between 33-66%. For a plain disomy, the ratio should be around50%. Without the use of a linkage model or an accurate error model ofthe average, this method would miss many cases of paternal trisomy. Incontrast, the method disclosed herein assigns parental genotypeprobabilities for each parental genotypic candidate, based on availablegenotypic information and population frequency, and does not explicitlyrequire parental genotypes. Additionally, the method disclosed herein isable to detect trisomy even in the absence or presence of parentgenotypic data, and can compensate by identifying the points of possiblecrossovers from matched to unmatched trisomy using a linkage model.

Some methods known in the art claim a method for averaging allele ratiosfrom SNPs where neither the maternal or paternal genotype is known, andfor determining the ploidy calls from average ratio on these SNPs.However, a method to accomplish these ends is not disclosed. The methoddisclosed herein is able to make accurate ploidy calls in such asituation, and the reduction to practice is disclosed elsewhere in thisdocument, using a joint probability maximum likelihood method andoptionally utilizes SNP noise and bias models, as well as a linkagemodel.

Some methods known in the art involve averaging allele ratios and claimto determine the ploidy calls from the average allele ratio at one or afew SNPs. However, such methods do not utilize the concept of linkage.The methods disclosed herein do not suffer from these drawbacks.

Using Sequence Length as a Prior to Determine the Origin of DNA

It has been reported that the distribution of length of sequences differfor maternal and fetal DNA, with fetal generally being shorter. In anembodiment of the present disclosure, it is possible to use previousknowledge in the form of empirical data, and construct priordistribution for expected length of both mother (P(X|maternal)) andfetal DNA (P(X|fetal)). Given new unidentified DNA sequence of length x,it is possible to assign a probability that a given sequence of DNA iseither maternal or fetal DNA, based on prior likelihood of x giveneither maternal or fetal. In particular if P(x|maternal)>P(x|fetal),then the DNA sequence can be classified as maternal, withP(x|maternal)=P(x|maternal)/[(P(x|maternal)+P(x|fetal)], and ifp(x|maternal)<p(x|fetal), then the DNA sequence can be classified asfetal, P(x|fetal)=P(x|fetal)/[(P(x|maternal)+P(x|fetal)]. In anembodiment of the present disclosure, a distributions of maternal andfetal sequence lengths can be determined that is specific for thatsample by considering the sequences that can be assigned as maternal orfetal with high probability, and then that sample specific distributioncan be used as the expected size distribution for that sample.

Variable Read Depth to Minimize Sequencing Cost

In many clinical trials concerning a diagnostic, for example, in Chiu etal. BMJ 2011; 342:c7401, a protocol with a number of parameters is set,and then the same protocol is executed with the same parameters for eachof the patients in the trial. In the case of determining the ploidystatus of a fetus gestating in a mother using sequencing as a method tomeasure genetic material one pertinent parameter is the number of reads.The number of reads may refer to the number of actual reads, the numberof intended reads, fractional lanes, full lanes, or full flow cells on asequencer. In these studies, the number of reads is typically set at alevel that will ensure that all or nearly all of the samples achieve thedesired level of accuracy. Sequencing is currently an expensivetechnology, a cost of roughly $200 per 5 mappable million reads, andwhile the price is dropping, any method which allows a sequencing baseddiagnostic to operate at a similar level of accuracy but with fewerreads will necessarily save a considerable amount of money.

The accuracy of a ploidy determination is typically dependent on anumber of factors, including the number of reads and the fraction offetal DNA in the mixture. The accuracy is typically higher when thefraction of fetal DNA in the mixture is higher. At the same time, theaccuracy is typically higher if the number of reads is greater. It ispossible to have a situation with two cases where the ploidy state isdetermined with comparable accuracies wherein the first case has a lowerfraction of fetal DNA in the mixture than the second, and more readswere sequenced in the first case than the second. It is possible to usethe estimated fraction of fetal DNA in the mixture as a guide indetermining the number of reads necessary to achieve a given level ofaccuracy.

In an embodiment of the present disclosure, a set of samples can be runwhere different samples in the set are sequenced to different readsdepths, wherein the number of reads run on each of the samples is chosento achieve a given level of accuracy given the calculated fraction offetal DNA in each mixture. In an embodiment of the present disclosure,this may entail making a measurement of the mixed sample to determinethe fraction of fetal DNA in the mixture; this estimation of the fetalfraction may be done with sequencing, it may be done with TAQMAN, it maybe done with qPCR, it may be done with SNP arrays, it may be done withany method that can distinguish different alleles at a given loci. Theneed for a fetal fraction estimate may be eliminated by includinghypotheses that cover all or a selected set of fetal fractions in theset of hypotheses that are considered when comparing to the actualmeasured data. After the fraction fetal DNA in the mixture has beendetermined, the number of sequences to be read for each sample may bedetermined.

In an embodiment of the present disclosure, 100 pregnant women visittheir respective OB's, and their blood is drawn into blood tubes with ananti-lysant and/or something to inactivate DNAase. They each take home akit for the father of their gestating fetus who gives a saliva sample.Both sets of genetic materials for all 100 couples are sent back to thelaboratory, where the mother blood is spun down and the buffy coat isisolated, as well as the plasma. The plasma comprises a mixture ofmaternal DNA as well as placentally derived DNA. The maternal buffy coatand the paternal blood is genotyped using a SNP array, and the DNA inthe maternal plasma samples are targeted with SURESELECT hybridizationprobes. The DNA that was pulled down with the probes is used to generate100 tagged libraries, one for each of the maternal samples, where eachsample is tagged with a different tag. A fraction from each library iswithdrawn, each of those fractions are mixed together and added to twolanes of a ILLUMINA HISEQ DNA sequencer in a multiplexed fashion,wherein each lane resulted in approximately 50 million mappable reads,resulting in approximately 100 million mappable reads on the 100multiplexed mixtures, or approximately 1 million reads per sample. Thesequence reads were used to determine the fraction of fetal DNA in eachmixture. 50 of the samples had more than 15% fetal DNA in the mixture,and the 1 million reads were sufficient to determine the ploidy statusof the fetuses with a 99.9% confidence.

Of the remaining mixtures, 25 had between 10 and 15% fetal DNA; afraction of each of the relevant libraries prepped from these mixtureswere multiplexed and run down one lane of the HISEQ generating anadditional 2 million reads for each sample. The two sets of sequencedata for each of the mixture with between 10 and 15% fetal DNA wereadded together, and the resulting 3 million reads per sample which weresufficient to determine the ploidy state of those fetuses with 99.9%confidence.

Of the remaining mixtures, 13 had between 6 and 10% fetal DNA; afraction of each of the relevant libraries prepped from these mixtureswere multiplexed and run down one lane of the HISEQ generating anadditional 4 million reads for each sample. The two sets of sequencedata for each of the mixture with between 6 and 10% fetal DNA were addedtogether, and the resulting 5 million total reads per mixture which weresufficient to determine the ploidy state of those fetuses with 99.9%confidence.

Of the remaining mixtures, 8 had between 4 and 6% fetal DNA; a fractionof each of the relevant libraries prepped from these mixtures weremultiplexed and run down one lane of the HISEQ generating an additional6 million reads for each sample. The two sets of sequence data for eachof the mixture with between 4 and 6% fetal DNA were added together, andthe resulting 7 million total reads per mixture which were sufficient todetermine the ploidy state of those fetuses with 99.9% confidence.

Of the remaining four mixtures, all of them had between 2 and 4% fetalDNA; a fraction of each of the relevant libraries prepped from thesemixtures were multiplexed and run down one lane of the HISEQ generatingan additional 12 million reads for each sample. The two sets of sequencedata for each of the mixture with between 2 and 4% fetal DNA were addedtogether, and the resulting 13 million total reads per mixture whichwere sufficient to determine the ploidy state of those fetuses with99.9% confidence.

This method required six lanes of sequencing on a HISEQ machine toachieve 99.9% accuracy over 100 samples. If the same number of runs hadbeen required for every sample, to ensure that every ploidydetermination was made with a 99.9% accuracy, it would have taken 25lanes of sequencing, and if a no-call rate or error rate of 4% wastolerated, it could have been achieved with 14 lanes of sequencing.

Using Raw Genotyping Data

There are a number of methods that can accomplish NPD using fetalgenetic information measured on fetal DNA found in maternal blood. Someof these methods involve making measurements of the fetal DNA using SNParrays, some methods involve untargeted sequencing, and some methodsinvolve targeted sequencing. The targeted sequencing may target SNPs, itmay target STRs, it may target other polymorphic loci, it may targetnon-polymorphic loci, or some combination thereof. Some of these methodsmay involve using a commercial or proprietary allele caller that callsthe identity of the alleles from the intensity data that comes from thesensors in the machine doing the measuring. For example, the ILLUMINAINFINIUM system or the AFFYMETRIX GENECHIP microarray system involvesbeads or microchips with attached DNA sequences that can hybridize tocomplementary segments of DNA; upon hybridization, there is a change inthe fluorescent properties of the sensor molecule that can be detected.There are also sequencing methods, for example the ILLUMINA SOLEXAGENOME SEQUENCER or the ABI SOLID GENOME SEQUENCER, wherein the geneticsequence of fragments of DNA are sequenced; upon extension of the strandof DNA complementary to the strand being sequenced, the identity of theextended nucleotide is typically detected via a fluorescent or radio tagappended to the complementary nucleotide. In all of these methods thegenotypic or sequencing data is typically determined on the basis offluorescent or other signals, or the lack thereof. These systems aretypically combined with low level software packages that make specificallele calls (secondary genetic data) from the analog output of thefluorescent or other detection device (primary genetic data). Forexample, in the case of a given allele on a SNP array, the software willmake a call, for example, that a certain SNP is present or not presentif the fluorescent intensity is measure above or below a certainthreshold. Similarly, the output of a sequencer is a chromatogram thatindicates the level of fluorescence detected for each of the dyes, andthe software will make a call that a certain base pair is A or T or C orG. High throughput sequencers typically make a series of suchmeasurements, called a read, that represents the most likely structureof the DNA sequence that was sequenced. The direct analog output of thechromatogram is defined here to be the primary genetic data, and thebase pair/SNP calls made by the software are considered here to be thesecondary genetic data. In an embodiment, primary data refers to the rawintensity data that is the unprocessed output of a genotyping platform,where the genotyping platform may refer to a SNP array, or to asequencing platform. The secondary genetic data refers to the processedgenetic data, where an allele call has been made, or the sequence datahas been assigned base pairs, and/or the sequence reads have been mappedto the genome.

Many higher level applications take advantage of these allele calls, SNPcalls and sequence reads, that is, the secondary genetic data, that thegenotyping software produces. For example, DNA NEXUS, ELAND or MAQ willtake the sequencing reads and map them to the genome. For example, inthe context of non-invasive prenatal diagnosis, complex informatics,such as PARENTAL SUPPORT™, may leverage a large number of SNP calls todetermine the genotype of an individual. Also, in the context ofpreimplantation genetic diagnosis, it is possible to take a set ofsequence reads that are mapped to the genome, and by taking a normalizedcount of the reads that are mapped to each chromosome, or section of achromosome, it may be possible to determine the ploidy state of anindividual. In the context of non-invasive prenatal diagnosis it may bepossible to take a set of sequence reads that have been measured on DNApresent in maternal plasma, and map them to the genome. One may thentake a normalized count of the reads that are mapped to each chromosome,or section of a chromosome, and use that data to determine the ploidystate of an individual. For example, it may be possible to conclude thatthose chromosomes that have a disproportionately large number of readsare trisomic in the fetus that is gestating in the mother from which theblood was drawn.

However, in reality, the initial output of the measuring instruments isan analog signal. When a certain base pair is called by the softwarethat is associated with the sequencing software, for example thesoftware may call the base pair a T, in reality the call is the callthat the software believes to be most likely. In some cases, however,the call may be of low confidence, for example, the analog signal mayindicate that the particular base pair is only 90% likely to be a T, and10% likely to be an A. In another example, the genotype calling softwarethat is associated with a SNP array reader may call a certain allele tobe G. However, in reality, the underlying analog signal may indicatethat it is only 70% likely that the allele is G, and 30% likely that theallele is T. In these cases, when the higher level applications use thegenotype calls and sequence calls made by the lower level software, theyare losing some information. That is, the primary genetic data, asmeasured directly by the genotyping platform, may be messier than thesecondary genetic data that is determined by the attached softwarepackages, but it contains more information. In mapping the secondarygenetic data sequences to the genome, many reads are thrown out becausesome bases are not read with enough clarity and or mapping is not clear.When the primary genetic data sequence reads are used, all or many ofthose reads that may have been thrown out when first converted tosecondary genetic data sequence read can be used by treating the readsin a probabilistic manner.

In an embodiment of the present disclosure, the higher level softwaredoes not rely on the allele calls, SNP calls, or sequence reads that aredetermined by the lower level software. Instead, the higher levelsoftware bases its calculations on the analog signals directly measuredfrom the genotyping platform. In an embodiment of the presentdisclosure, an informatics based method such as PARENTAL SUPPORT™ ismodified so that its ability to reconstruct the genetic data of theembryo/fetus/child is engineered to directly use the primary geneticdata as measured by the genotyping platform. In an embodiment of thepresent disclosure, an informatics based method such as PARENTALSUPPORT™ is able to make allele calls, and/or chromosome copy numbercalls using primary genetic data, and not using the secondary geneticdata. In an embodiment of the present disclosure, all genetic calls,SNPs calls, sequence reads, sequence mapping is treated in aprobabilistic manner by using the raw intensity data as measureddirectly by the genotyping platform, rather than converting the primarygenetic data to secondary genetic calls. In an embodiment, the DNAmeasurements from the prepared sample used in calculating allele countprobabilities and determining the relative probability of eachhypothesis comprise primary genetic data.

In some embodiments, the method can increase the accuracy of geneticdata of a target individual which incorporates genetic data of at leastone related individual, the method comprising obtaining primary geneticdata specific to a target individual's genome and genetic data specificto the genome(s) of the related individual(s), creating a set of one ormore hypotheses concerning possibly which segments of which chromosomesfrom the related individual(s) correspond to those segments in thetarget individual's genome, determining the probability of each of thehypotheses given the target individual's primary genetic data and therelated individual(s)'s genetic data, and using the probabilitiesassociated with each hypothesis to determine the most likely state ofthe actual genetic material of the target individual. In someembodiments, the method can determining the number of copies of asegment of a chromosome in the genome of a target individual, the methodcomprising creating a set of copy number hypotheses about how manycopies of the chromosome segment are present in the genome of a targetindividual, incorporating primary genetic data from the targetindividual and genetic information from one or more related individualsinto a data set, estimating the characteristics of the platform responseassociated with the data set, where the platform response may vary fromone experiment to another, computing the conditional probabilities ofeach copy number hypothesis, given the data set and the platformresponse characteristics, and determining the copy number of thechromosome segment based on the most probable copy number hypothesis. Inan embodiment, a method of the present disclosure can determine a ploidystate of at least one chromosome in a target individual, the methodcomprising obtaining primary genetic data from the target individual andfrom one or more related individuals, creating a set of at least oneploidy state hypothesis for each of the chromosomes of the targetindividual, using one or more expert techniques to determine astatistical probability for each ploidy state hypothesis in the set, foreach expert technique used, given the obtained genetic data, combining,for each ploidy state hypothesis, the statistical probabilities asdetermined by the one or more expert techniques, and determining theploidy state for each of the chromosomes in the target individual basedon the combined statistical probabilities of each of the ploidy statehypotheses. In an embodiment, a method of the present disclosure candetermine an allelic state in a set of alleles, in a target individual,and from one or both parents of the target individual, and optionallyfrom one or more related individuals, the method comprising obtainingprimary genetic data from the target individual, and from the one orboth parents, and from any related individuals, creating a set of atleast one allelic hypothesis for the target individual, and for the oneor both parents, and optionally for the one or more related individuals,where the hypotheses describe possible allelic states in the set ofalleles, determining a statistical probability for each allelichypothesis in the set of hypotheses given the obtained genetic data, anddetermining the allelic state for each of the alleles in the set ofalleles for the target individual, and for the one or both parents, andoptionally for the one or more related individuals, based on thestatistical probabilities of each of the allelic hypotheses.

In some embodiments, the genetic data of the mixed sample may comprisesequence data wherein the sequence data may not uniquely map to thehuman genome. In some embodiments, the genetic data of the mixed samplemay comprise sequence data wherein the sequence data maps to a pluralityof locations in the genome, wherein each possible mapping is associatedwith a probability that the given mapping is correct. In someembodiments, the sequence reads are not assumed to be associated with aparticular position in the genome. In some embodiments, the sequencereads are associated with a plurality of positions in the genome, and anassociated probability belonging to that position.

Combining Methods of Prenatal Diagnosis

There are many methods that may be used for prenatal diagnosis orprenatal screening of aneuploidy or other genetic defects. Describedelsewhere in this document, and in U.S. Utility application Ser. No.11/603,406, filed Nov. 28, 2006; U.S. Utility application Ser. No.12/076,348, filed Mar. 17, 2008, and PCT Utility Application Serial No.PCT/S09/52730 is one such method that uses the genetic data of relatedindividuals to increase the accuracy with which genetic data of a targetindividual, such as a fetus, is known, or estimated. Other methods usedfor prenatal diagnosis involve measuring the levels of certain hormonesin maternal blood, where those hormones are correlated with variousgenetic abnormalities. An example of this is called the triple test, atest wherein the levels of several (commonly two, three, four or five)different hormones are measured in maternal blood. In a case wheremultiple methods are used to determine the likelihood of a givenoutcome, where none of the methods are definitive in and of themselves,it is possible to combine the information given by those methods to makea prediction that is more accurate than any of the individual methods.In the triple test, combining the information given by the threedifferent hormones can result in a prediction of genetic abnormalitiesthat is more accurate than the individual hormone levels may predict.

Disclosed herein is a method for making more accurate predictions aboutthe genetic state of a fetus, specifically the possibility of geneticabnormalities in a fetus, that comprises combining predictions ofgenetic abnormalities in a fetus where those predictions were made usinga variety of methods. A “more accurate” method may refer to a method fordiagnosing an abnormality that has a lower false negative rate at agiven false positive rate. In a favored embodiment of the presentdisclosure, one or more of the predictions are made based on the geneticdata known about the fetus, where the genetic knowledge was determinedusing the PARENTAL SUPPORT™ method, that is, using genetic data ofindividual related to the fetus to determine the genetic data of thefetus with greater accuracy. In some embodiments the genetic data mayinclude ploidy states of the fetus. In some embodiments, the geneticdata may refer to a set of allele calls on the genome of the fetus. Insome embodiments some of the predictions may have been made using thetriple test. In some embodiments, some of the predictions may have beenmade using measurements of other hormone levels in maternal blood. Insome embodiments, predictions made by methods considered diagnoses maybe combined with predictions made by methods considered screening. Insome embodiments, the method involves measuring maternal blood levels ofalpha-fetoprotein (AFP). In some embodiments, the method involvesmeasuring maternal blood levels of unconjugated estriol (UE3). In someembodiments, the method involves measuring maternal blood levels of betahuman chorionic gonadotropin (beta-hCG). In some embodiments, the methodinvolves measuring maternal blood levels of invasive trophoblast antigen(ITA). In some embodiments, the method involves measuring maternal bloodlevels of inhibin. In some embodiments, the method involves measuringmaternal blood levels of pregnancy-associated plasma protein A (PAPP-A).In some embodiments, the method involves measuring maternal blood levelsof other hormones or maternal serum markers. In some embodiments, someof the predictions may have been made using other methods. In someembodiments, some of the predictions may have been made using a fullyintegrated test such as one that combines ultrasound and blood test ataround 12 weeks of pregnancy and a second blood test at around 16 weeks.In some embodiments, the method involves measuring the fetal nuchaltranslucency (NT). In some embodiments, the method involves using themeasured levels of the aforementioned hormones for making predictions.In some embodiments the method involves a combination of theaforementioned methods.

There are many ways to combine the predictions, for example, one couldconvert the hormone measurements into a multiple of the median (MoM) andthen into likelihood ratios (LR). Similarly, other measurements could betransformed into LRs using the mixture model of NT distributions. TheLRs for NT and the biochemical markers could be multiplied by the ageand gestation-related risk to derive the risk for various conditions,such as trisomy 21. Detection rates (DRs) and false-positive rates(FPRs) could be calculated by taking the proportions with risks above agiven risk threshold.

In an embodiment, a method to call the ploidy state involves combiningthe relative probabilities of each of the ploidy hypotheses determinedusing the joint distribution model and the allele count probabilitieswith relative probabilities of each of the ploidy hypotheses that arecalculated using statistical techniques taken from other methods thatdetermine a risk score for a fetus being trisomic, including but notlimited to: a read count analysis, comparing heterozygosity rates, astatistic that is only available when parental genetic information isused, the probability of normalized genotype signals for certain parentcontexts, a statistic that is calculated using an estimated fetalfraction of the first sample or the prepared sample, and combinationsthereof.

Another method could involve a situation with four measured hormonelevels, where the probability distribution around those hormones isknown: p(x₁, x₂, x₃, x₄₁e) for the euploid case and p(x₁, x₂, x₃, x₄₁a)for the aneuploid case. Then one could measure the probabilitydistribution for the DNA measurements, g(y|e) and g(y|a) for the euploidand aneuploid cases respectively. Assuming they are independent giventhe assumption of euploid/aneuploid, one could combine as p(x₁, x₂, x₃,x₄|a)g(y|a) and p(x₁, x₂, x₃, x₄|e)g(y|e) and then multiply each by theprior p(a) and p(e) given the maternal age. One could then choose theone that is highest.

In an embodiment, it is possible to evoke central limit theorem toassume distribution on g(y|a ore) is Gaussian, and measure mean andstandard deviation by looking at multiple samples. In anotherembodiment, one could assume they are not independent given the outcomeand collect enough samples to estimate the joint distribution p(x₁, x₂,x₃, x₄|a or e).

In an embodiment, the ploidy state for the target individual isdetermined to be the ploidy state that is associated with the hypothesiswhose probability is the greatest. In some cases, one hypothesis willhave a normalized, combined probability greater than 90%. Eachhypothesis is associated with one, or a set of, ploidy states, and theploidy state associated with the hypothesis whose normalized, combinedprobability is greater than 90%, or some other threshold value, such as50%, 80%, 95%, 98%, 99%, or 99.9%, may be chosen as the thresholdrequired for a hypothesis to be called as the determined ploidy state.

DNA from Children from Previous Pregnancies in Maternal Blood

One difficulty to non-invasive prenatal diagnosis is differentiatingfetal cells from the current pregnancy from fetal cells from previouspregnancies. Some believe that genetic matter from prior pregnancieswill go away after some time, but conclusive evidence has not beenshown. In an embodiment of the present disclosure, it is possible todetermine fetal DNA present in the maternal blood of paternal origin(that is, DNA that the fetus inherited from the father) using thePARENTAL SUPPORT™ (PS) method, and the knowledge of the paternal genome.This method may utilize phased parental genetic information. It ispossible to phase the parental genotype from unphased genotypicinformation using grandparental genetic data (such as measured geneticdata from a sperm from the grandfather), or genetic data from other bornchildren, or a sample of a miscarriage. One could also phase unphasedgenetic information by way of a HapMap-based phasing, or a haplotypingof paternal cells. Successful haplotyping has been demonstrated byarresting cells at phase of mitosis when chromosomes are tight bundlesand using microfluidics to put separate chromosomes in separate wells.In another embodiment it is possible to use the phased parentalhaplotypic data to detect the presence of more than one homolog from thefather, implying that the genetic material from more than one child ispresent in the blood. By focusing on chromosomes that are expected to beeuploid in a fetus, one could rule out the possibility that the fetuswas afflicted with a trisomy. Also, it is possible to determine if thefetal DNA is not from the current father, in which case one could useother methods such as the triple test to predict genetic abnormalities.

There may be other sources of fetal genetic material available viamethods other than a blood draw. In the case of the fetal geneticmaterial available in maternal blood, there are two main categories: (1)whole fetal cells, for example, nucleated fetal red blood cells orerythroblats, and (2) free floating fetal DNA. In the case of wholefetal cells, there is some evidence that fetal cells can persist inmaternal blood for an extended period of time such that it is possibleto isolate a cell from a pregnant woman that contains the DNA from achild or fetus from a prior pregnancy. There is also evidence that thefree floating fetal DNA is cleared from the system in a matter of weeks.One challenge is how to determine the identity of the individual whosegenetic material is contained in the cell, namely to ensure that themeasured genetic material is not from a fetus from a prior pregnancy. Inan embodiment of the present disclosure, the knowledge of the maternalgenetic material can be used to ensure that the genetic material inquestion is not maternal genetic material. There are a number of methodsto accomplish this end, including informatics based methods such asPARENTAL SUPPORT™, as described in this document or any of the patentsreferenced in this document.

In an embodiment of the present disclosure, the blood drawn from thepregnant mother may be separated into a fraction comprising freefloating fetal DNA, and a fraction comprising nucleated red blood cells.The free floating DNA may optionally be enriched, and the genotypicinformation of the DNA may be measured. From the measured genotypicinformation from the free floating DNA, the knowledge of the maternalgenotype may be used to determine aspects of the fetal genotype. Theseaspects may refer to ploidy state, and/or a set of allele identities.Then, individual nucleated red blood cells may be genotyped usingmethods described elsewhere in this document, and other referentpatents, especially those mentioned in the first section of thisdocument. The knowledge of the maternal genome would allow one todetermine whether or not any given single blood cell is geneticallymaternal. And the aspects of the fetal genotype that were determined asdescribed above would allow one to determine if the single blood cell isgenetically derived from the fetus that is currently gestating. Inessence, this aspect of the present disclosure allows one to use thegenetic knowledge of the mother, and possibly the genetic informationfrom other related individuals, such as the father, along with themeasured genetic information from the free floating DNA found inmaternal blood to determine whether an isolated nucleated cell found inmaternal blood is either (a) genetically maternal, (b) genetically fromthe fetus currently gestating, or (c) genetically from a fetus from aprior pregnancy.

Prenatal Sex Chromosome Aneuploidy Determination

In methods known in the art, people attempting to determine the sex of agestating fetus from the blood of the mother have used the fact thatfetal free floating DNA (fffDNA) is present in the plasma of the mother.If one is able to detect Y-specific loci in the maternal plasma, thisimplies that the gestating fetus is a male. However, the lack ofdetection of Y-specific loci in the plasma does not always guaranteethat the gestating fetus is a female when using methods known in theprior art, as in some cases the amount of fffDNA is too low to ensurethat the Y-specific loci would be detected in the case of a male fetus.

Presented here is a novel method that does not require the measurementof Y-specific nucleic acids, that is, DNA that is from loci that areexclusively paternally derived. The PARENTAL SUPPORT method, disclosedpreviously, uses crossover frequency data, parental genotypic data, andinformatics techniques, to determine the ploidy state of a gestatingfetus. The sex of a fetus is simply the ploidy state of the fetus at thesex chromosomes. A child that is XX is female, and XY is male. Themethod described herein is also able to determine the ploidy state ofthe fetus. Note that sexing is effectively synonymous with ploidydetermination of the sex chromosomes; in the case of sexing, anassumption is often made that the child is euploid, therefore there arefewer possible hypotheses.

The method disclosed herein involves looking at loci that are common toboth the X and Y chromosome to create a baseline in terms of expectedamount of fetal DNA present for a fetus. Then, those regions that arespecific only to the X chromosome can be interrogated to determine ifthe fetus is female or male. In the case of a male, we expect to seeless fetal DNA from loci that are specific to the X chromosome than fromloci that are specific to both the X and the Y. In contrast, in femalefetuses, we expect the amount of DNA for each of these groups to be thesame. The DNA in question can be measured by any technique that canquantitate the amount of DNA present on a sample, for example, qPCR, SNParrays, genotyping arrays, or sequencing. For DNA that is exclusivelyfrom an individual we would expect to see the following:

DNA specific to X DNA specific DNA specific to X and Y to Y Male (XY) A2A A Female (XX) 2A 2A 0In the case of DNA from a fetus that is mixed with DNA from the mother,and where the fraction of fetal DNA in the mixture is F, and where thefraction of maternal DNA in the mixture is M, such that F+M=100%, wewould expect to see the following:

DNA specific DNA specific to DNA specific to X X and Y to Y Male fetus(XY) M + ½F M + F ½F Female fetus (XX) M + F M + F 0In the case where F and M are known, the expected ratios can becomputed, and the observed data can be compared to the expected data. Inthe case where M and F are not known, a threshold can be selected basedon historical data. In both cases, the measured amount of DNA at locispecific to both X and Y can be used as a baseline, and the test for thesex of the fetus can be based on the amount of DNA observed on locispecific to only the X chromosome. If that amount is lower than thebaseline by an amount roughly equal to ½ F, or by an amount that causesit to fall below a predefined threshold, the fetus is determined to bemale, and if that amount is about equal to the baseline, or if is notlower by an amount that causes it to fall below a predefined threshold,the fetus is determined to be female.

In another embodiment, one can look only at those loci that are commonto both the X and the Y chromosomes, often termed the Z chromosome. Asubset of the loci on the Z chromosome are typically always A on the Xchromosome, and B on the Y chromosome. If SNPs from the Z chromosome arefound to have the B genotype, then the fetus is called a male; if theSNPs from the Z chromosome are found to only have A genotype, then thefetus is called a female. In another embodiment, one can look at theloci that are found only on the X chromosome. Contexts such as AA|B areparticularly informative as the presence of a B indicates that the fetushas an X chromosome from the father. Contexts such as AB|B are alsoinformative, as we expect to see B present only half as often in thecase of a female fetus as compared to a male fetus. In anotherembodiment, one can look at the SNPs on the Z chromosome where both Aand B alleles are present on both the X and the Y chromosome, and wherethe it is known which SNPs are from the paternal Y chromosome, and whichare from the paternal X chromosome.

In an embodiment, it is possible to amplify single nucleotide positionsknown to varying between the homologous non-recombining (HNR) regionshared by chromosome Y and chromosome X. The sequence within this HNRregion is largely identical between the X and Y chromosomes. Within thisidentical region are single nucleotide positions that, while invariantamong X chromosomes and among Y chromosomes in the population, aredifferent between the X and Y chromosomes. Each PCR assay could amplifya sequence from loci that are present on both the X and Y chromosomes.Within each amplified sequence would be a single base that can bedetected using sequencing or some other method.

In an embodiment, the sex of the fetus could be determined from thefetal free floating DNA found in maternal plasma, the method comprisingsome or all of the following steps: 1) Design PCR (either regular ormini-PCR, plus multiplexing if desired) primers amplify X/Y variantsingle nucleotide positions within HNR region, 2) obtain maternalplasma, 3) PCR Amplify targets from maternal plasma using HNR X/Y PCRassays, 4) sequence the amplicons, 5) Examine sequence data for presenceof Y-allele within one or more of the amplified sequences. The presenceof one or more would indicate a male fetus. Absence of all Y-allelesfrom all amplicons indicates a female fetus.

In an embodiment, one could use targeted sequencing to measure the DNAin the maternal plasma and/or the parental genotypes. In an embodiment,one could ignore all sequences that clearly originate from paternallysourced DNA. For example, in the context AAIAB, one could count thenumber of A sequences and ignore all the B sequences. In order todetermine a heterozygosity rate for the above algorithm, one couldcompare the number of observed A sequences to the expected number oftotal sequences for the given probe. There are many ways one couldcalculate an expected number of sequences for each probe on a per samplebasis. In an embodiment, it is possible to use historical data todetermine what fraction of all sequence reads belongs to each specificprobe and then use this empirical fraction, combined with the totalnumber of sequence reads, to estimate the number of sequences at eachprobe. Another approach could be to target some known homozygous allelesand then use historical data to relate the number of reads at each probewith the number of reads at the known homozygous alleles. For eachsample, one could then measure the number of reads at the homozygousalleles and then use this measurement, along with the empiricallyderived relationships, to estimate the number of sequence reads at eachprobe.

In some embodiments, it is possible to determine the sex of the fetus bycombining the predictions made by a plurality of methods. In someembodiments the plurality of methods are taken from methods described inthis disclosure. In some embodiments, at least one of the plurality ofmethods are taken from methods described in this disclosure.

In some embodiments the method described herein can be used to determinethe ploidy state of the gestating fetus. In an embodiment, the ploidycalling method uses loci that are specific to the X chromosome, orcommon to both the X and Y chromosome, but does not make use of anyY-specific loci. In an embodiment, the ploidy calling method uses one ormore of the following: loci that are specific to the X chromosome, locithat are common to both the X and Y chromosome, and loci that arespecific to the Y chromosome. In an embodiment, where the ratios of sexchromosomes are similar, for example 45,X (Turner Syndrome), 46,XX(normal female) and 47,XXX (trisomy X), the differentiation can beaccomplished by comparing the allele distributions to expected alleledistributions according to the various hypotheses. In anotherembodiment, this can be accomplished by comparing the relative number ofsequence reads for the sex chromosomes to one or a plurality ofreference chromosomes that are assumed to be euploid. Also note thatthese methods can be expanded to include aneuploid cases.

Single Gene Disease Screening

In an embodiment, a method for determining the ploidy state of the fetusmay be extended to enable simultaneous testing for single genedisorders. Single-gene disease diagnosis leverages the same targetedapproach used for aneuploidy testing, and requires additional specifictargets. In an embodiment, the single gene NPD diagnosis is throughlinkage analysis. In many cases, direct testing of the cfDNA sample isnot reliable, as the presence of maternal DNA makes it virtuallyimpossible to determine if the fetus has inherited the mother'smutation. Detection of a unique paternally-derived allele is lesschallenging, but is only fully informative if the disease is dominantand carried by the father, limiting the utility of the approach. In anembodiment, the method involves PCR or related amplification approaches.

In some embodiments, the method involves phasing the abnormal allelewith surrounding very tightly linked SNPs in the parents usinginformation from first-degree relatives. Then PARENTAL SUPPORT may berun on the targeted sequencing data obtained from these SNPs todetermine which homologs, normal or abnormal, were inherited by thefetus from both parents. As long as the SNPs are sufficiently linked,the inheritance of the genotype of the fetus can be determined veryreliably. In some embodiments, the method comprises (a) adding a set ofSNP loci to densely flank a specified set of common diseases to ourmultiplex pool for aneuploidy testing; (b) reliably phasing the allelesfrom these added SNPs with the normal and abnormal alleles based ongenetic data from various relatives; and (c) reconstructing the fetaldiplotype, or set of phased SNP alleles on the inherited maternal andpaternal homologs in the region surrounding the disease locus todetermine fetal genotype. In some embodiments additional probes that areclosely linked to a disease linked locus are added to the set ofpolymorphic locus being used for aneuploidy testing.

Reconstructing fetal diplotype is challenging because the sample is amixture of maternal and fetal DNA. In some embodiments, the methodincorporates relative information to phase the SNPs and disease alleles,then take into account physical distance of the SNPs and recombinationdata from location specific recombination likelihoods and the dataobserved from the genetic measurements of the maternal plasma to obtainthe most likely genotype of the fetus.

In an embodiment, a number of additional probes per disease linked locusare included in the set of targeted polymorphic loci; the number ofadditional probes per disease linked locus may be between 4 and 10,between 11 and 20, between 21 and 40, between 41 and 60, between 61 and80, or combinations thereof.

Determining the Number of DNA Molecules in a Sample.

A method is described herein to determine the number of DNA molecules ina sample by generating a uniquely identified molecule for each originalDNA molecules in the sample during the first round of DNA amplification.Described here is a procedure to accomplish the above end followed by asingle molecule or clonal sequencing method.

The approach entails targeting one or more specific loci and generatinga tagged copy of the original molecules such manner that most or all ofthe tagged molecules from each targeted locus will have a unique tag andcan be distinguished from one another upon sequencing of this barcodeusing clonal or single molecule sequencing. Each unique sequencedbarcode represents a unique molecule in the original sample.Simultaneously, sequencing data is used to ascertain the locus fromwhich the molecule originates. Using this information one can determinethe number of unique molecules in the original sample for each locus.

This method can be used for any application in which quantitativeevaluation of the number of molecules in an original sample is required.Furthermore, the number of unique molecules of one or more targets canbe related to the number of unique molecules to one or more othertargets to determine the relative copy number, allele distribution, orallele ratio. Alternatively, the number of copies detected from varioustargets can be modeled by a distribution in order to identify the mostlylikely number of copies of the original targets. Applications includebut are not limited to detection of insertions and deletions such asthose found in carriers of Duchenne Muscular Dystrophy; quantitation ofdeletions or duplications segments of chromosomes such as those observedin copy number variants; chromosome copy number of samples from bornindividuals; chromosome copy number of samples from unborn individualssuch as embryos or fetuses.

The method can be combined with simultaneous evaluation of variationscontained in the targeted by sequence. This can be used to determine thenumber of molecules representing each allele in the original sample.This copy number method can be combined with the evaluation of SNPs orother sequence variations to determine the chromosome copy number ofborn and unborn individuals; the discrimination and quantification ofcopies from loci which have short sequence variations, but in which PCRmay amplifies from multiple target regions such as in carrier detectionof Spinal Muscle Atrophy; determination of copy number of differentsources of molecules from samples consisting of mixtures of differentindividual such as in detection of fetal aneuploidy from free floatingDNA obtained from maternal plasma.

In an embodiment, the method as it pertains to a single target locus maycomprise one or more of the following steps: (1) Designing a standardpair of oligomers for PCR amplification of a specific locus. (2) Adding,during synthesis, a sequence of specified bases with no or minimalcomplementarity to the target locus or genome to the 5′ end of the oneof the target specific oligomer. This sequence, termed the tail, is aknown sequence, to be used for subsequent amplification, followed by asequence of random nucleotides. These random nucleotides comprise therandom region. The random region comprises a randomly generated sequenceof nucleic acids that probabilistically differ between each probemolecule. Consequently, following synthesis, the tailed oligomer poolwill consist of a collection of oligomers beginning with a knownsequence followed by unknown sequence that differs between molecules,followed by the target specific sequence. (3) Performing one round ofamplification (denaturation, annealing, extension) using only the tailedoligomer. (4) adding exonuclease to the reaction, effectively stoppingthe PCR reaction, and incubating the reaction at the appropriatetemperature to remove forward single stranded oligos that did not annealto temple and extend to form a double stranded product. (5) Incubatingthe reaction at a high temperature to denature the exonuclease andeliminate its activity. (6) Adding to the reaction a new oligonucleotidethat is complementary to tail of the oligomer used in the first reactionalong with the other target specific oligomer to enable PCRamplification of the product generated in the first round of PCR. (7)Continuing amplification to generate enough product for downstreamclonal sequencing. (8) Measuring the amplified PCR product by amultitude of methods, for example, clonal sequencing, to a sufficientnumber of bases to span the sequence.

In an embodiment, a method of the present disclosure involves targetingmultiple loci in parallel or otherwise. Primers to different target locican be generated independently and mixed to create multiplex PCR pools.In an embodiment, original samples can be divided into subpools anddifferent loci can be targeted in each sub-pool before being recombinedand sequenced. In an embodiment, the tagging step and a number ofamplification cycles may be performed before the pool is subdivided toensure efficient targeting of all targets before splitting, andimproving subsequent amplification by continuing amplification usingsmaller sets of primers in subdivided pools.

One example of an application where this technology would beparticularly useful is non-invasive prenatal aneuploidy diagnosis wherethe ratio of alleles at a given locus or a distribution of alleles at anumber of loci can be used to help determine the number of copies of achromosome present in a fetus. In this context, it is desirable toamplify the DNA present in the initial sample while maintaining therelative amounts of the various alleles. In some circumstances,especially in cases where there is a very small amount of DNA, forexample, fewer than 5,000 copies of the genome, fewer than 1,000 copiesof the genome, fewer than 500 copies of the genome, and fewer than 100copies of the genome, one can encounter a phenomenon calledbottlenecking. This is where there are a small number of copies of anygiven allele in the initial sample, and amplification biases can resultin the amplified pool of DNA having significantly different ratios ofthose alleles than are in the initial mixture of DNA. By applying aunique or nearly unique set of barcodes to each strand of DNA beforestandard PCR amplification, it is possible to exclude n−1 copies of DNAfrom a set of n identical molecules of sequenced DNA that originatedfrom the same original molecule.

For example, imagine a heterozygous SNP in the genome of an individual,and a mixture of DNA from the individual where ten molecules of eachallele are present in the original sample of DNA. After amplificationthere may be 100,000 molecules of DNA corresponding to that locus. Dueto stochastic processes, the ratio of DNA could be anywhere from 1:2 to2:1, however, since each of the original molecules was tagged with aunique tag, it would be possible to determine that the DNA in theamplified pool originated from exactly 10 molecules of DNA from eachallele. This method would therefore give a more accurate measure of therelative amounts of each allele than a method not using this approach.For methods where it is desirable for the relative amount of allele biasto be minimized, this method will provide more accurate data.

Association of the sequenced fragment to the target locus can beachieved in a number of ways. In an embodiment, a sequence of sufficientlength is obtained from the targeted fragment to span the moleculebarcode as well a sufficient number of unique bases corresponding to thetarget sequence to allow unambiguous identification of the target locus.In another embodiment, the molecular bar-coding primer that contains therandomly generated molecular barcode can also contain a locus specificbarcode (locus barcode) that identifies the target to which it is to beassociated. This locus barcode would be identical among all molecularbar-coding primers for each individual target and hence all resultingamplicons, but different from all other targets. In an embodiment, thetagging method described herein may be combined with a one-sided nestingprotocol.

In an embodiment, the design and generation of molecular barcodingprimers may be reduced to practice as follows: the molecular barcodingprimers may consist of a sequence that is not complementary to thetarget sequence followed by random molecular barcode region followed bya target specific sequence. The sequence 5′ of molecular barcode may beused for subsequence PCR amplification and may comprise sequences usefulin the conversion of the amplicon to a library for sequencing. Therandom molecular barcode sequence could be generated in a multitude ofways. The preferred method synthesizes the molecule tagging primer insuch a way as to include all four bases to the reaction during synthesisof the barcode region. All or various combinations of bases may bespecified using the IUPAC DNA ambiguity codes. In this manner thesynthesized collection of molecules will contain a random mixture ofsequences in the molecular barcode region. The length of the barcoderegion will determine how many primers will contain unique barcodes. Thenumber of unique sequences is related to the length of the barcoderegion as N^(L) where N is the number of bases, typically 4, and L isthe length of the barcode. A barcode of five bases can yield up to 1024unique sequences; a barcode of eight bases can yield 65536 uniquebarcodes. In an embodiment, the DNA can be measured by a sequencingmethod, where the sequence data represents the sequence of a singlemolecule. This can include methods in which single molecules aresequenced directly or methods in which single molecules are amplified toform clones detectable by the sequence instrument, but that stillrepresent single molecules, herein called clonal sequencing.

Some Embodiments

In some embodiments, a method is disclosed herein for generating areport disclosing the determined ploidy status of a chromosome in agestating fetus, the method comprising: obtaining a first sample thatcontains DNA from the mother of the fetus and DNA from the fetus;obtaining genotypic data from one or both parents of the fetus;preparing the first sample by isolating the DNA so as to obtain aprepared sample; measuring the DNA in the prepared sample at a pluralityof polymorphic loci; calculating, on a computer, allele counts or allelecount probabilities at the plurality of polymorphic loci from the DNAmeasurements made on the prepared sample; creating, on a computer, aplurality of ploidy hypotheses concerning expected allele countprobabilities at the plurality of polymorphic loci on the chromosome fordifferent possible ploidy states of the chromosome; building, on acomputer, a joint distribution model for allele count probability ofeach polymorphic locus on the chromosome for each ploidy hypothesisusing genotypic data from the one or both parents of the fetus;determining, on a computer, a relative probability of each of the ploidyhypotheses using the joint distribution model and the allele countprobabilities calculated for the prepared sample; calling the ploidystate of the fetus by selecting the ploidy state corresponding to thehypothesis with the greatest probability; and generating a reportdisclosing the determined ploidy status.

In some embodiments, the method is used to determine the ploidy state ofa plurality of gestating fetuses in a plurality of respective mothers,the method further comprising: determining the percent of DNA that is offetal origin in each of the prepared samples; and wherein the step ofmeasuring the DNA in the prepared sample is done by sequencing a numberof DNA molecules in each of the prepared samples, where more moleculesof DNA are sequenced from those prepared samples that have a smallerfraction of fetal DNA than those prepared samples that have a largerfraction of fetal DNA.

In some embodiments, the method is used to determine the ploidy state ofa plurality of gestating fetuses in a plurality of respective mothers,and where the measuring the DNA in the prepared sample is done, for eachof the fetuses, by sequencing a first fraction of the prepared sample ofDNA to give a first set of measurements, the method further comprising:making a first relative probability determination for each of the ploidyhypotheses for each of the fetuses, given the first set of DNAmeasurements; resequencing a second fraction of the prepared sample fromthose fetuses where the first relative probability determination foreach of the ploidy hypotheses indicates that a ploidy hypothesiscorresponding to an aneuploid fetus has a significant but not conclusiveprobability, to give a second set of measurements; making a secondrelative probability determination for ploidy hypotheses for the fetusesusing the second set of measurements and optionally also the first setof measurements; and calling the ploidy states of the fetuses whosesecond sample was resequenced by selecting the ploidy statecorresponding to the hypothesis with the greatest probability asdetermined by the second relative probability determination.

In some embodiments, a composition of matter is disclosed, thecomposition of matter comprising: a sample of preferentially enrichedDNA, wherein the sample of preferentially enriched DNA has beenpreferentially enriched at a plurality of polymorphic loci from a firstsample of DNA, wherein the first sample of DNA consisted of a mixture ofmaternal DNA and fetal DNA derived from maternal plasma, where thedegree of enrichment is at least a factor of 2, and wherein the allelicbias between the first sample and the preferentially enriched sample is,on average, selected from the group consisting of less than 2%, lessthan 1%, less than 0.5%, less than 0.2%, less than 0.1%, less than0.05%, less than 0.02%, and less than 0.01%. In some embodiments, amethod is disclosed to create a sample of such preferentially enrichedDNA.

In some embodiment, a method is disclosed for determining the presenceor absence of a fetal aneuploidy in a maternal tissue sample comprisingfetal and maternal genomic DNA, wherein the method comprises: (a)obtaining a mixture of fetal and maternal genomic DNA from said maternaltissue sample; (b) selectively enriching the mixture of fetal andmaternal DNA at a plurality of polymorphic alleles; (c) distributingselectively enriched fragments from the mixture of fetal and maternalgenomic DNA of step a to provide reaction samples comprising a singlegenomic DNA molecule or amplification products of a single genomic DNAmolecule; (d) conducting massively parallel DNA sequencing of theselectively enriched fragments of genomic DNA in the reaction samples ofstep c) to determine the sequence of said selectively enrichedfragments; (e) identifying the chromosomes to which the sequencesobtained in step d) belong; (f) analyzing the data of step d) todetermine i) the number of fragments of genomic DNA from step d) thatbelong to at least one first target chromosome that is presumed to bediploid in both the mother and the fetus, and ii) the number offragments of genomic DNA from step d) that belong to a second targetchromosome, wherein said second chromosome is suspected to be aneuploidin the fetus; (g) calculating an expected distribution of the number offragments of genomic DNA from step d) for the second target chromosomeif the second target chromosome is euploid, using the number determinedin step f) part i); (h) calculating an expected distribution of thenumber of fragments of genomic DNA from step d) for the second targetchromosome if the second target chromosome is aneuploid, using the firstnumber is step f) part i) and an estimated fraction of fetal DNA foundin the mixture of step b); and (i) using a maximum likelihood or maximuma posteriori approach to determine whether the number of fragments ofgenomic DNA determined in step f) part ii) is more likely to be part ofthe distribution calculated in step g) or the distribution calculated instep h); thereby indicating the presence or absence of a fetalaneuploidy.

EXPERIMENTAL SECTION

The presently disclosed embodiments are described in the followingExamples, which are set forth to aid in the understanding of thedisclosure, and should not be construed to limit in any way the scope ofthe disclosure as defined in the claims which follow thereafter. Thefollowing examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how touse the described embodiments, and are not intended to limit the scopeof the disclosure nor are they intended to represent that theexperiments below are all or the only experiments performed. Effortshave been made to ensure accuracy with respect to numbers used (e.g.amounts, temperature, etc.) but some experimental errors and deviationsshould be accounted for. Unless indicated otherwise, parts are parts byvolume, and temperature is in degrees Centigrade. It should beunderstood that variations in the methods as described may be madewithout changing the fundamental aspects that the experiments are meantto illustrate.

Experiment 1

The objective was to show that a Bayesian maximum likelihood estimation(MLE) algorithm that uses parent genotypes to calculate fetal fractionimproves accuracy of non-invasive prenatal trisomy diagnosis compared topublished methods.

Simulated sequencing data for maternal cfDNA was created by samplingreads obtained on trisomy-21 and respective mother cell lines. The rateof correct disomy and trisomy calls were determined from 500 simulationsat various fetal fractions for a published method (Chiu et al. BMJ 2011;342:c7401) and our MLE-based algorithm. We validated the simulations byobtaining 5 million shotgun reads from four pregnant mothers andrespective fathers collected under an IRB-approved protocol. Parentalgenotypes were obtained on a 290K SNP array. (See FIG. 14)

In simulations, the MLE-based approach achieved 99.0% accuracy for fetalfractions as low as 9% and reported confidences that corresponded wellto overall accuracy. We validated these results using four real sampleswherein we obtained all correct calls with a computed confidenceexceeding 99%. In contrast, our implementation of the publishedalgorithm for Chiu et al. required 18% fetal fraction to achieve 99.0%accuracy, and achieved only 87.8% accuracy at 9% fetal DNA.

Fetal fraction determination from parental genotypes in conjunction witha MLE-based approach achieves greater accuracy than published algorithmsat the fetal fractions expected during the 1st and early 2nd trimester.Furthermore, the method disclosed herein produces a confidence metricthat is crucial in determining the reliability of the result, especiallyat low fetal fractions where ploidy detection is more difficult.Published methods use a less accurate threshold method for callingploidy based on large sets of disomy training data, an approach thatpredefines a false positive rate. In addition, without a confidencemetric, published methods are at risk of reporting false negativeresults when there is insufficient fetal cfDNA to make a call. In someembodiments, a confidence estimate is calculated for the called ploidystate.

Experiment 2

The objective was to improve non-invasive detection of fetal trisomy 18,21, and X particularly in samples consisting of low fetal fraction byusing a targeted sequencing approach combined with parent genotypes andHapmap data in a Bayesian Maximum Likelihood Estimation (MLE) algorithm.

Maternal samples from four euploid and two trisomy-positive pregnanciesand respective paternal samples were obtained under an IRB-approvedprotocol from patients where fetal karyotype was known. Maternal cfDNAwas extracted from plasma and roughly 10 million sequence reads wereobtained following preferential enrichment that targeted specific SNPs.Parent samples were similarly sequenced to obtain genotypes.

The described algorithm correctly called chromosome 18 and 21 disomy forall euploid samples and normal chromosomes of aneuploid samples. Trisomy18 and 21 calls were correct, as were chromosome X copy numbers in maleand female fetuses. The confidence produced by the algorithm was inexcess of 98% in all cases.

The method described accurately reported the ploidy of all testedchromosomes from six samples, including samples comprised of less than12% fetal DNA, which account for roughly 30% of 1^(st) and early2^(nd)-trimester samples. The crucial difference between the instant MLEalgorithm and published methods is that it leverages parent genotypesand Hapmap data to improve accuracy and generate a confidence metric. Atlow fetal fractions, all methods become less accurate; it is importantto correctly identify samples without sufficient fetal cfDNA to make areliable call. Others have used chromosome Y specific probes to estimatefetal fraction of male fetuses, but concurrent parental genotypingenables estimation of fetal fraction for both sexes. Another inherentlimitation of published methods using untargeted shotgun sequencing isthat accuracy of ploidy calling varies among chromosomes due todifferences in factors such as GC richness. The instant targetedsequencing approach is largely independent of such chromosome-scalevariations and yields more consistent performance between chromosomes.

Experiment 3

The objective was to determine if trisomy is detectable with highconfidence on a triploid fetus, using novel informatics to analyze SNPloci of free floating fetal DNA in maternal plasma.

20 mL of blood was drawn from a pregnant patient following abnormalultrasound. After centrifugation, maternal DNA was extracted from thebuffy coat (DNEASY, QIAGEN); cell-free DNA was extracted from plasma(QIAAMP QIAGEN). Targeted sequencing was applied to SNP loci onchromosomes 2, 21, and X in both DNA samples. Maximum-LikelihoodBayesian estimation selected the most likely hypothesis from the set ofall possible ploidy states. The method determines fetal DNA fraction,ploidy state and explicit confidences in the ploidy determination. Noassumptions are made about the ploidy of a reference chromosome. Thediagnostic uses a test statistic that is independent of sequence readcounts, which is the recent state of the art.

The instant method accurately diagnosed trisomy of chromosomes 2 and 21.Child fraction was estimated at 11.9% [CI 11.7-12.1]. The fetus wasfound to have one maternal and two paternal copies of chromosomes 2 and21 with confidence of effectively 1 (error probability<10⁻³⁰). This wasachieved with 92,600 and 258,100 reads on chromosomes 2 and 21respectively.

This is the first demonstration of non-invasive prenatal diagnosis oftrisomic chromosomes from maternal blood where the fetus was triploid,as confirmed by metaphase karyotype. Extant methods of non-invasivediagnosis would not detect aneuploidy in this sample. Current methodsrely on a surplus of sequence reads on a trisomic chromosome relative todisomic reference chromosomes; but a triploid fetus has no disomicreference. Furthermore, extant methods would not achieve similarlyhigh-confidence ploidy determination with this fraction of fetal DNA andnumber of sequence reads. It is straightforward to extend the approachto all 24 chromosomes.

Experiment 4

The following protocol was used for 800-plex amplification of DNAisolated from maternal plasma from a euploid pregnancy and also genomicDNA from a triploidy 21 cell line using standard PCR (meaning no nestingwas used). Library preparation and amplification involved single tubeblunt ending followed by A-tailing. Adaptor ligation was run using theligation kit found in the AGILENT SURESELECT kit, and PCR was run for 7cycles. Then, 15 cycles of STA (95° C. for 30s; 72° C. for 1 min; 60° C.for 4 min; 65° C. for 1 min; 72° C. for 30s) using 800 different primerpairs targeting SNPs on chromosomes 2, 21 and X. The reaction was runwith 12.5 nM primer concentration. The DNA was then sequenced with anILLUMINA IIGAX sequencer. The sequencer output 1.9 million reads, ofwhich 92% mapped to the genome; of those reads that mapped to thegenome, more than 99% mapped to one of the regions targeted by thetargeted primers. The numbers were essentially the same for both theplasma DNA and the genomic DNA. FIG. 15 shows the ratio of the twoalleles for the ˜780 SNPs that were detected by the sequencer in thegenomic DNA that was taken from a cell line with known trisomy atchromosome 21. Note that the allele ratios are plotted here for ease ofvisualization, because the allele distributions are not straightforwardto read visually. The circles represent

SNPs on disomic chromosomes, while the stars represent SNPs on atrisomic chromosome. FIG. 16 is another representation of the same dataas in Figure X, where the Y-axis is the relative number of A and Bmeasured for each SNP, and where the X-axis is the SNP number where theSNPs are separated by chromosome. In FIG. 16, SNP 1 to 312 are found onchromosome 2, from SNP 313 to 605 are found on chromosome 21 which istrisomic, and from SNP 606 to 800 are on chromosome X. The data fromchromosomes 2 and X show a disomic chromosome, as the relative sequencecounts lie in three clusters: AA at the top of the graph, BB at thebottom of the graph, and AB in the middle of the graph. The data fromchromosome 21, which is trisomic, shows four clusters: AAA at the top ofthe graph, AAB around the 0.65 line (⅔), ABB around the 0.35 line (⅓),and BBB at the bottom of the graph.

FIGS. 17A-17D show data for the same 800-plex protocol, but measured onDNA that was amplified from four plasma samples from pregnant women. Forthese four samples, we expect to see seven clusters of dots: (1) alongthe top of the graph are those loci where both the mother and the fetusare AA, (2) slightly below the top of the graph are those loci where themother is AA and the fetus is AB, (3) slightly above the 0.5 line arethose loci where the mother is AB and the fetus is AA, (4) along the 0.5line are those loci where the mother and the fetus are both AB, (5)slightly below the 0.5 line are those loci where the mother is AB andthe fetus is BB, (6) slightly above the bottom of the graph are thoseloci where the mother is BB and the fetus is AB, (7) along the bottom ofthe graph are those loci where both the mother and the fetus are BB. Thesmaller the fetal fraction, the less the separation between clusters (1)and (2), between clusters (3), (4) and (5), and between clusters (6) and(7). The separation is expected to be half of the fraction of DNA thatis of fetal origin. For example, if the DNA is 20% fetal, and 80%maternal, we expect (1) through (7) to be centered at 1.0, 0.9, 0.6,0.5, 0.4, 0.1 and 0.0 respectively; see for example FIG. 17D,POOL1_BC5_ref_rate. If, instead the DNA is 8% fetal, and 92% maternal,we expect (1) through (7) to be centered at 1.00, 0.96, 0.54, 0.50,0.46, 0.04 and 0.00 respectively; see for example FIG. 17B,POOL1_BC2_ref_rate. If there is not fetal DNA detected, we do not expectto see (2), (3), (5), or (6); alternately we could say that theseparation is zero, and therefore (1) and (2) are on top of each other,as are (3), (4) and (5), and also (6) and (7); see e.g. FIG. 17C,POOL1_BC7ref_rate. Note that the fetal fraction for FIG. 17A,POOL1_BC1_ref_rate is about 25%.

Experiment 5

Most methods of DNA amplification and measurement will produce someallele bias, wherein the two alleles that are typically found at a locusare detected with intensities or counts that are not representative ofthe actual amounts of alleles in the sample of DNA. For example, for asingle individual, at a heterozygous locus we expect to see a 1:1 ratioof the two alleles, which is the theoretical ratio expected for aheterozygous locus; however due to allele bias, we may see 55:45, oreven 60:40. Also note that in the context of sequencing, if the depth ofread is low, then simple stochastic noise could result in significantallele bias. In an embodiment, it is possible to model the behavior ofeach SNP such that if a consistent bias is observed for particularalleles, this bias can be corrected for. FIG. 18 shows the fraction ofdata that can be explained by binomial variance, before and after biascorrection. In FIG. 18, the stars represent the observed allele bias onraw sequence data for the 800-plex experiment; the circles represent theallele bias after correction. Note that if there were no allele bias atall, we would expect the data to fall along the x=y line. A similar setof data that was produced by amplifying DNA using a 150-plex targetedamplification produced data that fell very closely on the 1:1 line afterbias correction.

Experiment 6

Universal amplification of DNA using ligated adaptors with primersspecific to the adaptor tags, where the primer annealing and extensiontimes are limited to a few minutes has the effect of enriching theproportion of shorter DNA strands. Most library protocols designed forcreating DNA libraries suitable for sequencing contain such a step, andexample protocols are published and well known to those in the art. Insome embodiments of the invention, adaptors with a universal tag areligated to the plasma DNA, and amplified using primers specific to theadaptor tag. In some embodiments, the universal tag can be the same tagas used for sequencing, it can be a universal tag only for PCRamplification, or it can be a set of tags. Since the fetal DNA istypically short in nature, while the maternal DNA can be both short andlong in nature, this method has the effect of enriching the proportionof fetal DNA in the mixture. The free floating DNA, thought to be DNAfrom apoptotic cells, and which contains both fetal and maternal DNA, isshort—mostly under 200 bp. Cellular DNA released by cell lysis, a commonphenomenon after phlebotomy, is typically almost exclusively maternal,and is also quite long—mostly above 500 bp. Therefore, blood samplesthat have sat around for more than a few minutes will contain a mixtureof short (fetal+maternal) and longer (maternal) DNA. Performing auniversal amplification with relatively short extension times onmaternal plasma followed by targeted amplification will tend to increasethe relative proportion of fetal DNA when compared to the plasma thathas been amplified using targeted amplification alone. This can be seenin FIG. 19 which shows the measured fetal percent when the input isplasma DNA (vertical axis) vs. the measured fetal percent when the inputDNA is plasma DNA that has had a library prepared using the ILLUMINAGAIIx library preparation protocol. All the dots fall below the line,indicating that the library preparation step enriches the fraction ofDNA that is of fetal origin. Two samples of plasma that were red,indicating hemolysis and therefore that there would be an increasedamount of long maternal DNA present from cell lysis, show a particularlysignificant enrichment of fetal fraction when the library preparation isperformed prior to targeted amplification. The method disclosed hereinis particularly useful in cases where there is hemolysis or some othersituation has occurred where cells comprising relatively long strands ofcontaminating DNA have lysed, contaminating the mixed sample of shortDNA with the long DNA. Typically, the relatively short annealing andextension times are between 30 seconds and 2 minutes, though they couldbe as short as 5 or 10 seconds or less, or as long as 5 or 10 minutes.

Experiment 7

The following protocol was used for 1,200-plex amplification of DNAisolated from maternal plasma from a euploid pregnancy and also genomicDNA from a triploidy 21 cell line using a direct PCR protocol, and alsoa semi-nested approach. Library preparation and amplification involvedsingle tube blunt ending followed by A-tailing. Adaptor ligation was runusing a modification of the ligation kit found in the AGILENT SURESELECTkit, and PCR was run for 7 cycles. In the targeted primer pool, therewere 550 assays for SNPs from chromosome 21, and 325 assays for SNPsfrom each of chromosomes 1 and X. Both protocols involved 15 cycles ofSTA (95° C. for 30s; 72° C. for 1 min; 60° C. for 4 min; 65° C. for 30s;72° C. for 30s) using 16 nM primer concentration. The semi-nested PCRprotocol involved a second amplification of 15 cycles of STA (95° C. for30s; 72° C. for 1 min; 60° C. for 4 min; 65° C. for 30s; 72° C. for 30s)using an inner forward tag concentration of 29 nM, and a reverse tagconcentration of 1 uM or 0.1 uM. The DNA was then sequenced with anILLUMINA IIGAX sequencer. For the direct PCR protocol, 73% of the readsmap to the genome; for the semi-nested protocol, 97.2% of the sequencereads map to the genome. Therefore, the semi-nested protocol result inapproximately 30% more information, presumably mostly due to theelimination of primers that are most likely to cause primer dimers.

The depth of read variability tends to be higher when using thesemi-nested protocol than when the direct PCR protocol is used (see FIG.20) where the diamonds refer to the depth of read for loci run with thesemi-nested protocol, and the squares refer to the depth of read forloci run with no nesting. The SNPs are arranged by depth of read for thediamonds, so the diamonds all fall on a curved line, while the squaresappear to be loosely correlated; the arrangements of the SNPs isarbitrary, and it is the height of the dot that denotes depth of readrather than its location left to right.

In some embodiments, the methods described herein can achieve excellentdepth of read (DOR) variances. For example, in one version of thisexperiment (FIG. 21) using a 1,200-plex direct PCR amplification ofgenomic DNA, of the 1,200 assays: 1186 assays had a DOR greater than 10;the average depth of read was 400; 1063 assays (88.6%) had a depth ofread of between 200 and 800, and ideal window where the number of readsfor each allele is high enough to give meaningful data, while the numberof reads for each allele is not so high that the marginal use of thosereads was particularly small. Only 12 alleles had higher depth of readwith the highest at 1035 reads. The standard deviation of the DOR was290, the average DOR was 453, the coefficient of variance of the DOR was64%, there were 950,000 total reads, and 63.1% of the reads mapped tothe genome. In another experiment (FIG. 22) using a 1,200-plexsemi-nested protocol, the DOR was higher. The standard deviation of theDOR was 583, the average DOR was 630, the coefficient of variance of theDOR was 93%, there were 870,000 total reads, and 96.3% of the readsmapped to the genome. Note, in both these cases, the SNPs are arrangedby the depth of read for the mother, so the curved line represents thematernal depth of read. The differentiation between child and father isnot significant; it is only the trend that is significant for thepurpose of this explanation.

Experiment 8

In an experiment, the semi-nested 1,200-plex PCR protocol was used toamplify DNA from one cell and from three cells. This experiment isrelevant to prenatal aneuploidy testing using fetal cells isolated frommaternal blood, or for preimplantation genetic diagnosis using biopsiedblastomeres or trophectoderm samples. There were 3 replicates of 1 and 3cells from 2 individuals (46 XY and 47 XX+21) per condition. Assaystargeted chromosomes 1, 21 and X. Three different lysis methods wereused: ARCTURUS, MPERv2 and Alkaline lysis. Sequencing was runmultiplexing 48 samples in one sequencing lane. The algorithm returnedcorrect ploidy calls for each of the three chromosomes, and for each ofthe replicates.

Experiment 9

In one experiment, four maternal plasma samples were prepared andamplified using a hemi-nested 9,600-plex protocol. The samples wereprepared in the following way: Up to 40 mL of maternal blood werecentrifuged to isolate the buffy coat and the plasma. The genomic DNA inthe maternal and was prepared from the buffy coat and paternal DNA wasprepared from a blood sample or saliva sample. Cell-free DNA in thematernal plasma was isolated using the QIAGEN CIRCULATING NUCLEIC ACIDkit and eluted in 45 uL TE buffer according to manufacturer'sinstructions. Universal ligation adapters were appended to the end ofeach molecule of 35 uL of purified plasma DNA and libraries wereamplified for 7 cycles using adaptor specific primers. Libraries werepurified with AGENCOURT AMPURE beads and eluted in 50 ul water.

3 ul of the DNA was amplified with 15 cycles of STA (95° C. for 10 minfor initial polymerase activation, then 15 cycles of 95° C. for 30s; 72°C. for 10 s; 65° C. for 1 min; 60° C. for 8 min; 65° C. for 3 min and72° C. for 30s; and a final extension at 72° C. for 2 min) using 14.5 nMprimer concentration of 9600 target-specific tagged reverse primers andone library adaptor specific forward primer at 500 nM.

The hemi-nested PCR protocol involved a second amplification of adilution of the first STAs product for 15 cycles of STA (95° C. for 10min for initial polymerase activation, then 15 cycles of 95° C. for 30s;65° C. for 1 min; 60° C. for 5 min; 65° C. for 5 min and 72° C. for 30s;and a final extension at 72° C. for 2 min) using reverse tagconcentration of 1000 nM, and a concentration of 16.6 u nM for each of9600 target-specific forward primers.

An aliquot of the STA products was then amplified by standard PCR for 10cycles with 1 uM of tag-specific forward and barcoded reverse primers togenerate barcoded sequencing libraries. An aliquot of each library wasmixed with libraries of different barcodes and purified using a spincolumn.

In this way, 9,600 primers were used in the single-well reactions; theprimers were designed to target SNPs found on chromosomes 1, 2, 13, 18,21, X and Y. The amplicons were then sequenced using an ILLUMINA GAIIXsequencer. Per sample, approximately 3.9 million reads were generated bythe sequencer, with 3.7 million reads mapping to the genome (94%), andof those, 2.9 million reads (74%) mapped to targeted SNPs with anaverage depth of read of 344 and a median depth of read of 255. Thefetal fraction for the four samples was found to be 9.9%, 18.9%, 16.3%,and 21.2%

Relevant maternal and paternal genomic DNA samples amplified using asemi-nested 9600-plex protocol and sequenced. The semi-nested protocolis different in that it applies 9,600 outer forward primers and taggedreverse primers at 7.3 nM in the first STA. Thermocycling conditions andcomposition of the second STA, and the barcoding PCR were the same asfor the hemi-nested protocol.

The sequencing data was analyzed using informatics methods disclosedherein and the ploidy state was called at six chromosomes for thefetuses whose DNA was present in the 4 maternal plasma samples. Theploidy calls for all 28 chromosomes in the set were called correctlywith confidences above 99.2% except for one chromosome that was calledcorrectly, but with a confidence of 83%.

FIG. 23 shows the depth of read of the 9,600-plex hemi-nesting approachalong with the depth of read of the 1,200-plex semi-nested approachdescribed in Experiment 7, though the number of SNPs with a depth ofread greater than 100, greater than 200 and greater than 400 wassignificantly higher than in the 1,200-plex protocol. The number ofreads at the 90^(th) percentile can be divided by the number of reads atthe 10^(th) percentile to give a dimensionless metric that is indicativeof the uniformity of the depth of read; the smaller the number, the moreuniform (narrow) the depth of read. The average 90^(th)percentile/10^(th) percentile ratio is 11.5 for the method run inExperiment 9, while it is 5.6 for the method run in Experiment 7. Anarrower depth of read for a given protocol plexity is better forsequencing efficiency, as fewer sequence reads are necessary to ensurethat a certain percentage of reads are above a read number threshold.

Experiment 10

In one experiment, four maternal plasma samples were prepared andamplified using a semi-nested 9,600-plex protocol. Details of Experiment10 were very similar to Experiment 9, the exception being the nestingprotocol, and including the identity of the four samples. The ploidycalls for all 28 chromosomes in the set were called correctly withconfidences above 99.7%. 7.6 million (97%) of reads mapped to thegenome, and 6.3 million (80%) of the reads mapped to the targeted SNPs.The average depth of read was 751, and the median depth of read was 396.

Experiment 11

In one experiment, three maternal plasma samples were split into fiveequal portions, and each portion was amplified using either 2,400multiplexed primers (four portions) or 1,200 multiplexed primers (oneportion) and amplified using a semi-nested protocol, for a total of10,800 primers. After amplification, the portions were pooled togetherfor sequencing. Details of Experiment 11 were very similar to Experiment9, the exception being the nesting protocol, and the split and poolapproach. The ploidy calls for all 21 chromosomes in the set were calledcorrectly with confidences above 99.7%, except for one missed call wherethe confidence was 83%. 3.4 million reads mapped to targeted SNPs, theaverage depth of read was 404 and the median depth of read was 258.

Experiment 12

In one experiment, four maternal plasma samples were split into fourequal portions, and each portion was amplified using 2,400 multiplexedprimers and amplified using a semi-nested protocol, for a total of 9,600primers. After amplification, the portions were pooled together forsequencing. Details of Experiment 12 were very similar to Experiment 9,the exception being the nesting protocol, and the split and poolapproach. The ploidy calls for all 28 chromosomes in the set were calledcorrectly with confidences above 97%, except for one missed call wherethe confidence was 78%. 4.5 million reads mapped to targeted SNPs, theaverage depth of read was 535 and the median depth of read was 412.

Experiment 13

In one experiment, four maternal plasma samples were prepared andamplified using a 9,600-plex triply hemi-nested protocol, for a total of9,600 primers. Details of Experiment 12 were very similar to Experiment9, the exception being the nesting protocol which involved three roundsof amplification; the three rounds involved 15, 10 and 15 STA cyclesrespectively. The ploidy calls for 27 of 28 chromosomes in the set werecalled correctly with confidences above 99.9%, except for one that wascalled correctly with 94.6%, and one missed call with a confidence of80.8%. 3.5 million reads mapped to targeted SNPs, the average depth ofread was 414 and the median depth of read was 249.

Experiment 14

In one experiment 45 sets of cells were amplified using a 1,200-plexsemi-nested protocol, sequenced, and ploidy determinations were made atthree chromosomes. Note that this experiment is meant to simulate theconditions of performing pre-implantation genetic diagnosis onsingle-cell biopsies from day 3 embryos, or trophectoderm biopsies fromday 5 embryos. 15 individual single cells and 30 sets of three cellswere placed in 45 individual reaction tubes for a total of 45 reactionswhere each reaction contained cells from only one cell line, but thedifferent reactions contained cells from different cell lines. The cellswere prepared into 5 ul washing buffer and lysed the by adding 5 ulARCTURUS PICOPURE lysis buffer (APPLIED BIOSYSTEMS) and incubating at56° C. for 20 min, 95° C. for 10 min.

The DNA of the single/three cells was amplified with 25 cycles of STA(95° C. for 10 min for initial polymerase activation, then 25 cycles of95° C. for 30s; 72° C. for 10 s; 65° C. for 1 min; 60° C. for 8 min; 65°C. for 3 min and 72° C. for 30s; and a final extension at 72° C. for 2min) using 50 nM primer concentration of 1200 target-specific forwardand tagged reverse primers.

The semi-nested PCR protocol involved three parallel secondamplification of a dilution of the first STAs product for 20 cycles ofSTA (95° C. for 10 min for initial polymerase activation, then 15 cyclesof 95° C. for 30s; 65° C. for 1 min; 60° C. for 5 min; 65° C. for 5 minand 72° C. for 30s; and a final extension at 72° C. for 2 min) usingreverse tag specific primer concentration of 1000 nM, and aconcentration of 60 nM for each of 400 target-specific nested forwardprimers. In the three parallel 400-plex reactions the total of 1200targets amplified in the first STA were thus amplified.

An aliquot of the STA products was then amplified by standard PCR for 15cycles with 1 uM of tag-specific forward and barcoded reverse primers togenerate barcoded sequencing libraries. An aliquot of each library wasmixed with libraries of different barcodes and purified using a spincolumn.

In this way, 1,200 primers were used in the single cell reactions; theprimers were designed to target SNPs found on chromosomes 1, 21 and X.The amplicons were then sequenced using an ILLUMINA GAIIX sequencer. Persample, approximately 3.9 million reads were generated by the sequencer,with 500,000 to 800,000 million reads mapping to the genome (74% to 94%of all reads per sample).

Relevant maternal and paternal genomic DNA samples from cell lines wereanalyzed using the same semi-nested 1200-plex assay pool with a similarprotocol with fewer cycles and 1200-plex second STA, and sequenced.

The sequencing data was analyzed using informatics methods disclosedherein and the ploidy state was called at the three chromosomes for thesamples.

FIG. 24 shows normalized depth of read ratios (vertical axis) for sixsamples at three chromosomes (1=chrom 1; 2=chrom 21; 3=chrom X). Theratios were set to be equal to the number of reads mapping to thatchromosome, normalized, and divided by the number of reads mapping tothat chromosome averaged over three wells each comprising three 46XYcells. The three sets of data points corresponding to the 46XY reactionsare expected to have ratios of 1:1. The three sets of data pointscorresponding to the 47XX+21 cells are expected to have ratios of 1:1for chromosome 1, 1.5:1 for chromosome 21, and 2:1 for chromosome X.

FIGS. 25A-25C show allele ratios plotted for three chromosomes (1, 21,X) for three reactions. The reaction in the lower left shows a reactionon three 46XY cells (FIG. 25B). The left region are the allele ratiosfor chromosome 1, the middle region are the allele ratios for chromosome21, and the right region are the allele ratios for chromosome X. For the46XY cells, for chromosome 1 we expect to see ratios of 1, 0.5 and 0,corresponding to AA, AB and BB SNP genotypes. For the 46XY cells, forchromosome 21 we expect to see ratios of 1, 0.5 and 0, corresponding toAA, AB and BB SNP genotypes. For the 46XY cells, for chromosome X weexpect to see ratios of 1 and 0, corresponding to A, and B SNPgenotypes. The reaction in the lower right shows a reaction on three47XX+21 cells (FIG. 25C). The allele ratios are segregated by chromosomeas in the lower left graph. For the 47XX+21 cells, for chromosome 1 weexpect to see ratios of 1, 0.5 and 0, corresponding to AA, AB and BB SNPgenotypes. For the 47XX+21 cells, for chromosome 21 we expect to seeratios of 1, 0.67, 0.33 and 0, corresponding to AAA, AAB, ABB and BBBSNP genotypes. For the 47XX+21 cells, for chromosome X we expect to seeratios of 1, 0.5 and 0, corresponding to AA, AB, and BB SNP genotypes.The plot in the upper right was made on a reaction comprising 1 ng ofgenomic DNA from the 47XX+21 cell line (FIG. 25A). FIGS. 26A and 26Bshows the same graphs as in FIG. 25, but for reactions performed on onlyone cell. The left graph was a reaction that contained a 47XX+21 cell(FIG. 26A), and the right graph was for a reaction that contained a 46XXcell (FIG. 26B).

From the graphs shown in FIGS. 25A-25C and FIGS. 26A and 26B, it isvisually apparent that there are two clusters of dots for chromosomeswhere we expect to see ratios of 1 and 0; three clusters of dots forchromosomes where we expect to see ratios of 1, 0.5, and 0, and fourclusters of dots for chromosomes where we expect to see ratios of 1,0.67, 0.33 and 0. The PARENTAL SUPPORT algorithm was able to makecorrect calls on all of the three chromosomes for all of the 45reactions.

All patents, patent applications, and published references cited hereinare hereby incorporated by reference in their entirety. While themethods of the present disclosure have been described in connection withthe specific embodiments thereof, it will be understood that it iscapable of further modification. Furthermore, this application isintended to cover any variations, uses, or adaptations of the methods ofthe present disclosure, including such departures from the presentdisclosure as come within known or customary practice in the art towhich the methods of the present disclosure pertain, and as fall withinthe scope of the appended claims.

What is claimed is:
 1. A method for measuring an amount of DNA in abiological sample, the method comprising: (a) performing a multiplexamplification for at least 100 polymorphic loci on one or morechromosomes expected to be disomic in a single reaction mixture, whereinthe reaction mixture comprises cell-free DNA extracted from a biologicalsample of a subject comprising DNA of mixed origin, wherein the DNA ofmixed origin comprises DNA from the subject and DNA from a geneticallydistinct individual, and wherein the amplification comprises ligatingoligonucleotides that hybridize to target sequences comprising thepolymorphic loci and amplifying the ligated oligonucleotides by PCR; (b)measuring a quantity of each allele at a plurality of amplifiedpolymorphic loci that comprise an allele present in the geneticallydistinct individual but not the subject; (c) determining an amount ofthe DNA from the genetically distinct individual in the biologicalsample using the quantity of each allele at the polymorphic loci.
 2. Themethod of claim 1, further comprising determining a bias of themultiplex amplification or a bias of the microarray analysis, and usingthe bias to statistically correct the determined quantity of each alleleat the plurality of polymorphic loci on the one or more chromosomesexpected to be disomic before the quantity of each allele is used todetermine the amount of the DNA from the genetically distinctindividual.
 3. The method of claim 1, wherein the DNA of mixed origincomprises DNA from a transplant.
 4. The method of claim 1, wherein thepolymorphic loci are SNP loci.
 5. The method of claim 1, wherein thepolymorphic loci comprise more than 200 SNP loci.
 6. The method of claim1, wherein the polymorphic loci comprise more than 500 SNP loci.
 7. Themethod of claim 1, wherein the polymorphic loci comprise more than 1000SNP loci.
 8. The method of claim 1, wherein the polymorphic locicomprise more than 2000 SNP loci.
 9. The method of claim 1, wherein thepolymorphic loci comprise SNP loci on chromosome
 1. 10. The method ofclaim 1, wherein the polymorphic loci comprise SNP loci on chromosome 2.11. The method of claim 1, wherein the polymorphic loci comprise SNPloci on chromosome
 3. 12. The method of claim 1, wherein the biologicalsample is a blood, serum, plasma, or urine sample.
 13. The method ofclaim 1, wherein step (b) is performed by microarray.
 14. The method ofclaim 1, wherein step (b) is performed by high-throughput sequencing.15. A method for measuring an amount of DNA in a biological sample, themethod comprising: (a) performing a PCR and/or an allele-specificamplification for at least 100 SNP loci on one or more chromosomesexpected to be disomic in a single reaction mixture, wherein thereaction mixture comprises cell-free DNA extracted from a biologicalsample of a subject comprising DNA of mixed origin, wherein the DNA ofmixed origin comprises DNA from the subject and DNA from a geneticallydistinct individual, and wherein the amplification comprises ligatingoligonucleotides that hybridize to target sequences comprising the SNPloci and amplifying the ligated oligonucleotides; (b) measuring aquantity of each allele at a plurality of amplified SNP loci thatcomprise an allele present in the genetically distinct individual butnot the subject; (c) measuring an amount of the DNA from the geneticallydistinct individual in the biological sample using the quantity of eachallele at the SNP loci and an expected quantity of each allele at theSNP loci for different DNA fractions.
 16. The method of claim 15,further comprising determining a bias of the PCR and/or allele-specificamplification or a bias of the microarray analysis, and using the biasto statistically correct the determined quantity of each allele at theplurality of SNP loci on the one or more chromosomes expected to bedisomic before the quantity of each allele is used to determine theamount of the DNA from the genetically distinct individual.
 17. Themethod of claim 15, wherein the DNA of mixed origin comprises DNA from atransplant.
 18. The method of claim 15, wherein the biological sample isa blood, serum, plasma, or urine sample.
 19. The method of claim 15,wherein step (b) is performed by microarray.
 20. The method of claim 15,wherein step (b) is performed by high-throughput sequencing.